From 4ee4af028c43a681e3e9e38d2ad19c7124b9a603 Mon Sep 17 00:00:00 2001
From: eastzio <3156045992@qq.com>
Date: Mon, 27 Jul 2026 13:20:41 +0800
Subject: [PATCH] =?UTF-8?q?[sim]=20=E6=95=B4=E7=90=86=E5=90=8E=E6=9C=9FSim?=
 =?UTF-8?q?2Sim=E4=B8=8E=E6=AF=94=E8=B5=9BRough=E7=AD=96=E7=95=A5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                                    |    1 +
 01_doc/version_history.md                     |   10 +
 05_software/README.md                         |    2 +-
 05_software/train/README.md                   |    5 +-
 05_software/train/rc_mjlab/DEPENDENCIES.md    |    7 +
 05_software/train/rc_mjlab/README.md          |   12 +-
 05_software/train/rc_mjlab/model_6800.onnx    |  Bin 0 -> 777597 bytes
 05_software/train/rc_mjlab/sim2sim/README.md  |   58 +
 .../train/rc_mjlab/sim2sim/export_onnx.py     |  110 +
 .../rc_mjlab/sim2sim/ik_compensation_sweep.py |  238 ++
 .../rc_mjlab/sim2sim/ik_slalom_sim2sim.py     | 1235 ++++++++
 .../rc_mjlab/sim2sim/interface/mujoco_io.py   |   11 +-
 .../sim2sim/nav_route_sim2sim_check.py        | 2776 +++++++++++++++++
 .../train/rc_mjlab/sim2sim/nav_sim2sim.py     |    6 +-
 .../rc_mjlab/sim2sim/policy/policy_runner.py  |   37 +-
 .../train/rc_mjlab/sim2sim/requirements.txt   |    3 +
 05_software/train/rc_mjlab/sim2sim/sim2sim.py |   17 +-
 .../train/rc_mjlab/tools/nav_tools/README.md  |    7 +
 .../tools/nav_tools/route_safety_check.py     |  638 ++++
 README.md                                     |    3 +-
 20 files changed, 5161 insertions(+), 15 deletions(-)
 create mode 100644 05_software/train/rc_mjlab/model_6800.onnx
 create mode 100644 05_software/train/rc_mjlab/sim2sim/README.md
 create mode 100644 05_software/train/rc_mjlab/sim2sim/export_onnx.py
 create mode 100644 05_software/train/rc_mjlab/sim2sim/ik_compensation_sweep.py
 create mode 100644 05_software/train/rc_mjlab/sim2sim/ik_slalom_sim2sim.py
 create mode 100644 05_software/train/rc_mjlab/sim2sim/nav_route_sim2sim_check.py
 create mode 100644 05_software/train/rc_mjlab/sim2sim/requirements.txt
 create mode 100644 05_software/train/rc_mjlab/tools/nav_tools/README.md
 create mode 100644 05_software/train/rc_mjlab/tools/nav_tools/route_safety_check.py

diff --git a/.gitignore b/.gitignore
index 29a3009..ec9d871 100644
--- a/.gitignore
+++ b/.gitignore
@@ -36,6 +36,7 @@ checkpoints/
 wandb/
 sim2sim_log_*.txt
 **/sim2sim_temp.xml
+**/route_check_runs/
 
 # IDE and operating system files
 .idea/
diff --git a/01_doc/version_history.md b/01_doc/version_history.md
index e5a5a06..6970060 100644
--- a/01_doc/version_history.md
+++ b/01_doc/version_history.md
@@ -12,6 +12,7 @@
 | `v0.5.0` | 随机化增强 | 扩大观测、延迟和动力学随机化，加入持续外力扰动 |
 | `v0.6.0` | 比赛训练架构 | 分轴奖励、自适应指令课程、障碍释放课程和比赛站姿 |
 | `v0.7.0` | MuJoCo 工具 | 姿态优化、IK 扫描、动力学、MPC 和 GUI 调试工具 |
+| `v0.8.0` | 后期 Sim2Sim | ONNX 回放、IK/路线检查工具和比赛最终 Rough 策略 |
 
 ## `v0.4.0` 的模型变化
 
@@ -51,3 +52,12 @@
 - 增加 IK/差速轮参数扫描，可导出 JSON 结果。
 - 增加 Robot、Controller、Dynamics、MPCController 和 GUI 调试链路。
 - 记录历史工具常量与新版 MJCF 质量、比赛默认站姿之间的参数边界，避免将分析结果直接当作已校准真机参数。
+
+## `v0.8.0` 的后期 Sim2Sim
+
+- 比赛训练任务、MJCF 和 `v0.7.0` 的 MuJoCo 工具保持不变。
+- 策略运行器增加 ONNX 加载，并允许在缺少 `pynput` 时关闭后台键盘监听继续运行。
+- MuJoCo 执行器重建同时兼容新旧 Spec 删除接口。
+- 增加 PT→ONNX 导出、IK 补偿扫描、纯 IK 绕桩和 ONNX 批量路线检查入口。
+- 归档比赛最终 Rough 策略 `model_6800.onnx`；其 SHA-256 为 `3C994BDD3434AD15770A52AC0E8D229F502F00D6511CDD42C2E2C742301AEF13`。
+- Crawl 权重、运行日志、临时 XML 和大量重复路线实验不在本阶段归档。
diff --git a/05_software/README.md b/05_software/README.md
index 3ed6053..157bf58 100644
--- a/05_software/README.md
+++ b/05_software/README.md
@@ -28,7 +28,7 @@ MJCF + mjlab task
 IK real --------------------------------> 电机
 ```
 
-`rc_mjlab` 是自包含工程。训练、MJCF、MuJoCo、Sim2Sim 和策略权重通过相对路径绑定，因此保留其内部布局，没有为了目录外观拆散。第一代完整闭环见 `v0.3.0`，第一份新版 MJCF 与训练框架见 `v0.4.0`，随机化增强版见 `v0.5.0`，比赛最终训练架构见 `v0.6.0`，后期 MuJoCo 工具集见 `v0.7.0`。
+`rc_mjlab` 是自包含工程。训练、MJCF、MuJoCo、Sim2Sim 和策略权重通过相对路径绑定，因此保留其内部布局，没有为了目录外观拆散。第一代完整闭环见 `v0.3.0`，第一份新版 MJCF 与训练框架见 `v0.4.0`，随机化增强版见 `v0.5.0`，比赛最终训练架构见 `v0.6.0`，后期 MuJoCo 工具集见 `v0.7.0`，后期 Sim2Sim 与比赛 Rough 策略见 `v0.8.0`。
 
 详细说明见：
 
diff --git a/05_software/train/README.md b/05_software/train/README.md
index fbb3db4..4fb7831 100644
--- a/05_software/train/README.md
+++ b/05_software/train/README.md
@@ -2,7 +2,7 @@
 
 `rc_mjlab/` 保存 16DOF 轮足机器人的当前训练与 Sim2Sim 工程。历史快照由 Git Tag 保留，不在目录中复制 `old`、`new` 或 `final` 版本。
 
-当前内容对应 `v0.7.0`：训练代码保持 `v0.6.0` 的比赛架构，新增后期 MuJoCo 姿态、IK、动力学和 MPC 工具。训练过程可能先获得基模，再调整奖励、课程和环境参数继续训练；模型 checkpoint 的变化不等同于软件架构变化。
+当前内容对应 `v0.8.0`：训练代码保持 `v0.6.0` 的比赛架构，包含 `v0.7.0` 的 MuJoCo 独立工具，并新增后期 Sim2Sim、路线检查和比赛最终 Rough ONNX 策略。训练过程可能先获得基模，再调整奖励、课程和环境参数继续训练；模型 checkpoint 的变化不等同于软件架构变化。
 
 ## 内容
 
@@ -12,6 +12,7 @@
 - `mujoco_sim`：不依赖训练循环的姿态、IK、动力学和 MPC 分析
 - `mjlab`：固定版本的本地训练框架依赖
 - `model_rough.pt`：本阶段 Rough 策略权重
+- `model_6800.onnx`：比赛最终使用的 Rough 策略
 - `pyproject.toml`、`uv.lock`：Python 环境与依赖锁定
 
 与 `v0.3.0` 相比，本版本更新了 MJCF 质量和惯性参数，并将 mjlab 上游基准从 `00409797` 更新到 `40f8d93e`。机械 CAD 未发生变化。
@@ -22,4 +23,6 @@
 
 `v0.7.0` 不修改比赛训练架构，增加独立 MuJoCo 工具；入口和参数边界见 [`rc_mjlab/mujoco_sim/README.md`](rc_mjlab/mujoco_sim/README.md)。
 
+`v0.8.0` 继续保持训练架构和 MJCF 不变，归档后期 Sim2Sim 增量与比赛 Rough ONNX 策略；入口和归档边界见 [`rc_mjlab/sim2sim/README.md`](rc_mjlab/sim2sim/README.md)。
+
 工程命令和任务说明见 [`rc_mjlab/README.md`](rc_mjlab/README.md)，本地依赖来源见 [`rc_mjlab/DEPENDENCIES.md`](rc_mjlab/DEPENDENCIES.md)。
diff --git a/05_software/train/rc_mjlab/DEPENDENCIES.md b/05_software/train/rc_mjlab/DEPENDENCIES.md
index b3fb65e..768e85d 100644
--- a/05_software/train/rc_mjlab/DEPENDENCIES.md
+++ b/05_software/train/rc_mjlab/DEPENDENCIES.md
@@ -8,6 +8,7 @@
 - `mjlab[cu128]`
 - PyTorch CUDA 12.8 环境
 - `pynput`
+- 后期 Sim2Sim 可选依赖：Pygame、ONNX Runtime
 
 精确解析结果保存在 `uv.lock`。项目使用本地可编辑的 `mjlab`：
 
@@ -39,4 +40,10 @@ uv run train Robot-Flat-v0
 uv run play Robot-Rough-v0
 ```
 
+根 `uv.lock` 保留比赛训练环境的历史解析结果。后期 Sim2Sim 新增依赖单独保存在 `sim2sim/requirements.txt`，运行时叠加，避免重新锁定时升级历史 MuJoCo nightly：
+
+```bash
+uv run --with-requirements sim2sim/requirements.txt python sim2sim/nav_sim2sim.py
+```
+
 GPU、CUDA、MuJoCo development wheel 和驱动版本必须满足 `pyproject.toml` 与 `uv.lock` 的约束。
diff --git a/05_software/train/rc_mjlab/README.md b/05_software/train/rc_mjlab/README.md
index 283d13f..71d71b2 100644
--- a/05_software/train/rc_mjlab/README.md
+++ b/05_software/train/rc_mjlab/README.md
@@ -2,7 +2,7 @@
 
 基于 [mjlab](https://github.com/google-deepmind/mjlab) 框架的四轮腿混合机器人强化学习训练与部署部署项目，面向机器人竞赛场景（如越障、匍匐、斜坡、台阶等复合任务）。
 
-> 当前目录对应 `v0.7.0`：保留 `v0.6.0` 的比赛训练架构，并加入后期 MuJoCo 独立工具集。当前目录中的 `model_rough.pt` 是早期参考权重；比赛最终使用的 `model_6800.onnx` 将随最终部署版本归档。
+> 当前目录对应 `v0.8.0`：保留 `v0.6.0` 的比赛训练架构与 `v0.7.0` 的 MuJoCo 独立工具集，加入后期 Sim2Sim 工具和比赛最终 Rough 策略 `model_6800.onnx`。`model_rough.pt` 仍作为早期参考权重保留。
 
 ---
 
@@ -41,6 +41,10 @@ rc_mjlab/
 │       └── competition_terrains.py   # 竞赛自定义地形（高墙障碍、低杆障碍）
 ├── sim2sim/                      # Sim2Sim 物理部署与高精度交互回放工具
 │   ├── nav_sim2sim.py            # 主程序：2D Pygame 交互面板 + 全自动多地形导航追踪
+│   ├── nav_route_sim2sim_check.py # ONNX 策略批量路线检查
+│   ├── ik_slalom_sim2sim.py      # 纯 IK、路径跟踪与绕桩验证
+│   ├── ik_compensation_sweep.py  # IK 补偿参数扫描
+│   ├── export_onnx.py            # PT actor 导出与 ONNX 一致性检查
 │   ├── sim2sim.py                # 简易版键盘调试工具
 │   ├── interface/
 │   │   └── mujoco_io.py          # MuJoCo 输入输出与传感器、低通滤波器接口
@@ -54,7 +58,9 @@ rc_mjlab/
 │   ├── scene.xml                 # mjlab 场景入口文件
 │   └── meshes/                   # STL/OBJ 碰撞与外观网格
 ├── mujoco_sim/                   # 姿态、IK、动力学和 MPC 独立工具
-├── model_rough.pt                # 本阶段用于回放和 Sim2Sim 的 Rough 策略
+├── tools/nav_tools/              # 路线安全检查公共模块
+├── model_rough.pt                # 早期 Rough 参考 checkpoint
+├── model_6800.onnx               # 比赛最终 Rough 策略
 ├── pyproject.toml                # 项目依赖（uv 管理，含清华镜像源加速）
 └── uv.lock                       # 精确依赖锁定文件
 ```
@@ -89,6 +95,8 @@ cd sim2sim
 uv run python nav_sim2sim.py
 ```
 
+后期 Sim2Sim 的入口、模型边界和批量检查命令见 [`sim2sim/README.md`](sim2sim/README.md)。
+
 ---
 
 ## 🖥️ 交互式自动导航平台 (sim2sim/nav_sim2sim.py)
diff --git a/05_software/train/rc_mjlab/model_6800.onnx b/05_software/train/rc_mjlab/model_6800.onnx
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diff --git a/05_software/train/rc_mjlab/sim2sim/README.md b/05_software/train/rc_mjlab/sim2sim/README.md
new file mode 100644
index 0000000..8e43e94
--- /dev/null
+++ b/05_software/train/rc_mjlab/sim2sim/README.md
@@ -0,0 +1,58 @@
+# 后期 Sim2Sim 工具
+
+本目录保存比赛训练架构之后形成的 MuJoCo 策略验证工具。`v0.8.0` 在早期 Sim2Sim 基础上增加 ONNX 策略加载、IK 参数扫描、纯 IK 绕桩验证和批量路线检查；训练任务与 MJCF 不在本阶段修改。
+
+## 主要入口
+
+- `nav_sim2sim.py`：Pygame 面板与 MuJoCo 多任务导航，Rough 策略优先加载根目录的 `model_6800.onnx`。
+- `sim2sim.py`：较轻量的键盘控制与策略回放入口，优先加载 `model_6800.onnx`，缺失时回退到早期 `model_rough.pt`。
+- `ik_slalom_sim2sim.py`：不依赖 RL 策略的 IK、差速轮、路径跟踪和绕桩测试。
+- `ik_compensation_sweep.py`：批量扫描 IK 补偿参数并输出排序结果。
+- `nav_route_sim2sim_check.py`：使用 ONNX 策略批量检查内置任务或外部航点路线。
+- `export_onnx.py`：将兼容的 PyTorch actor checkpoint 导出并核对为 ONNX。
+- `interface/mujoco_io.py`：MuJoCo 模型、传感器和执行器接口。
+- `policy/policy_runner.py`：PT/ONNX 策略加载与历史观测缓存。
+
+## 环境
+
+主训练环境继续由根目录的 `uv.lock` 管理。后期 Sim2Sim 新增的 Pygame 与 ONNX Runtime 单独记录在 `sim2sim/requirements.txt`，运行时叠加，避免重新解析时改变已归档的 MuJoCo nightly 版本：
+
+```powershell
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\nav_sim2sim.py
+```
+
+训练工程提供 MuJoCo、NumPy、PyTorch、Matplotlib 和 `pynput`；专用 requirements 显式补充 Pygame 与 ONNX Runtime。下面其他命令同样使用 `--with-requirements .\sim2sim\requirements.txt`。
+
+## 常用命令
+
+```powershell
+# 比赛 Rough 策略交互回放
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\nav_sim2sim.py
+
+# 轻量策略回放
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\sim2sim.py
+
+# 纯 IK 绕桩验证
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\ik_slalom_sim2sim.py --test slalom
+
+# IK 补偿参数扫描
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\ik_compensation_sweep.py --top 12
+
+# 使用内置绕桩任务做批量 Sim2Sim 路线检查
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\nav_route_sim2sim_check.py `
+  --terrain-xml .\sim2sim\terrain\scene_terrain.xml `
+  --mission slalom `
+  --onnx .\model_6800.onnx
+
+# 导出早期参考 PT 权重；也可用 --pt-path 指定其他 checkpoint
+uv run --with-requirements .\sim2sim\requirements.txt python .\sim2sim\export_onnx.py
+```
+
+## 模型与边界
+
+- `../model_6800.onnx` 是 `last_not_slalom_1050` 最终真机工程使用的比赛 Rough 策略，SHA-256 为 `3C994BDD3434AD15770A52AC0E8D229F502F00D6511CDD42C2E2C742301AEF13`。
+- `../model_rough.pt` 是较早阶段的参考 checkpoint，两者不是同一版本的权重。
+- Crawl 模型未在本阶段归档；需要 Crawl 策略的入口会查找 `model_crawl.onnx` 或 `model_crawl.pt`。
+- `nav_route_sim2sim_check.py` 依赖 `../tools/nav_tools/route_safety_check.py` 的航点和避障几何定义。
+
+运行时生成的日志、临时 XML 和 `route_check_runs/` 不纳入版本库。源目录中的大量路线试验结果也未复制；它们包含重复轨迹和本机绝对路径，不属于可复用程序源码。
diff --git a/05_software/train/rc_mjlab/sim2sim/export_onnx.py b/05_software/train/rc_mjlab/sim2sim/export_onnx.py
new file mode 100644
index 0000000..d1be128
--- /dev/null
+++ b/05_software/train/rc_mjlab/sim2sim/export_onnx.py
@@ -0,0 +1,110 @@
+import argparse
+import torch
+import torch.nn as nn
+import numpy as np
+from pathlib import Path
+import onnxruntime as ort
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+
+class PolicyMLP(nn.Module):
+    def __init__(self, obs_dim=53, action_dim=16):
+        super().__init__()
+        self.register_buffer("obs_mean", torch.zeros(obs_dim))
+        self.register_buffer("obs_std", torch.ones(obs_dim))
+        self.net = nn.Sequential(
+            nn.Linear(obs_dim, 512), nn.ELU(),
+            nn.Linear(512, 256), nn.ELU(),
+            nn.Linear(256, 128), nn.ELU(),
+            nn.Linear(128, action_dim),
+        )
+
+    def forward(self, x):
+        x = (x - self.obs_mean) / torch.clamp(self.obs_std, min=1e-6)
+        return self.net(x)
+
+def load_policy(model_path, device):
+    ckpt = torch.load(model_path, map_location=device, weights_only=False)
+    state_dict = ckpt["actor_state_dict"]
+    weight_key = "mlp.0.weight" if "mlp.0.weight" in state_dict else "net.0.weight"
+    obs_dim = state_dict[weight_key].shape[1]
+
+    output_key = "mlp.6.weight" if "mlp.6.weight" in state_dict else "net.6.weight"
+    action_dim = state_dict[output_key].shape[0]
+
+    model = PolicyMLP(obs_dim=obs_dim, action_dim=action_dim)
+    my_sd = {}
+    for k, v in state_dict.items():
+        if k.startswith("mlp."):
+            my_sd[k.replace("mlp.", "net.")] = v
+        elif k.startswith("net."):
+            my_sd[k] = v
+        elif k == "obs_normalizer._mean":
+            my_sd["obs_mean"] = v.squeeze()
+        elif k == "obs_normalizer._var":
+            my_sd["obs_std"] = torch.sqrt(v.squeeze() + 1e-5)
+
+    model.load_state_dict(my_sd, strict=False)
+    model.eval()
+    model.to(device)
+    return model, obs_dim
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--pt-path",
+        "--pt_path",
+        dest="pt_path",
+        type=Path,
+        default=PROJECT_ROOT / "model_rough.pt",
+        help="PyTorch checkpoint to export (default: ../model_rough.pt).",
+    )
+    args = parser.parse_args()
+
+    pt_path = args.pt_path.expanduser().resolve()
+    if not pt_path.exists():
+        print(f"File not found: {pt_path}")
+        return
+
+    device = torch.device("cpu")
+    print(f"Loading {pt_path}...")
+    model, obs_dim = load_policy(pt_path, device)
+
+    onnx_path = pt_path.with_suffix(".onnx")
+
+    dummy_input = torch.randn(1, obs_dim, device=device)
+
+    print(f"Exporting to {onnx_path}...")
+    torch.onnx.export(
+        model,
+        dummy_input,
+        str(onnx_path),
+        export_params=True,
+        opset_version=14,
+        do_constant_folding=True,
+        input_names=["obs"],
+        output_names=["action"],
+        dynamic_axes={"obs": {0: "batch_size"}, "action": {0: "batch_size"}}
+    )
+
+    print("Verifying ONNX export...")
+    try:
+        session = ort.InferenceSession(str(onnx_path))
+        with torch.no_grad():
+            pt_out = model(dummy_input).numpy()
+        onnx_out = session.run(["action"], {"obs": dummy_input.numpy()})[0]
+
+        max_diff = np.max(np.abs(pt_out - onnx_out))
+        mean_diff = np.mean(np.abs(pt_out - onnx_out))
+        print(f"ONNX vs PyTorch - max_diff: {max_diff:.6f}, mean_diff: {mean_diff:.6f}")
+
+        if max_diff < 1e-4:
+            print("ONNX export verified OK.")
+        else:
+            print("WARNING: ONNX export has significant divergence from PyTorch model.")
+    except ImportError:
+        print("onnxruntime not installed. Skipping verification. Install with: pip install onnxruntime")
+
+if __name__ == "__main__":
+    main()
diff --git a/05_software/train/rc_mjlab/sim2sim/ik_compensation_sweep.py b/05_software/train/rc_mjlab/sim2sim/ik_compensation_sweep.py
new file mode 100644
index 0000000..f013f7b
--- /dev/null
+++ b/05_software/train/rc_mjlab/sim2sim/ik_compensation_sweep.py
@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+"""Sweep IK compensation parameters in the standalone sim2sim scene."""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+THIS_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = THIS_DIR.parent
+if str(THIS_DIR) not in sys.path:
+    sys.path.insert(0, str(THIS_DIR))
+
+SIM_PATH = THIS_DIR / "ik_slalom_sim2sim.py"
+spec = importlib.util.spec_from_file_location("ik_slalom_sim2sim", SIM_PATH)
+if spec is None or spec.loader is None:
+    raise RuntimeError(f"Cannot load {SIM_PATH}")
+sim = importlib.util.module_from_spec(spec)
+sys.modules["ik_slalom_sim2sim"] = sim
+spec.loader.exec_module(sim)
+
+
+TRIALS = [
+    {"name": "forward", "speed": 1.0, "yaw": 0.0, "target_vx": 1.0, "target_yaw": 0.0},
+    {"name": "yaw", "speed": 0.0, "yaw": 1.0, "target_vx": 0.0, "target_yaw": 1.0},
+    {"name": "arc", "speed": 1.0, "yaw": 1.0, "target_vx": 1.0, "target_yaw": 1.0},
+]
+
+
+def parse_float_list(text: str) -> list[float]:
+    return [float(x.strip()) for x in text.split(",") if x.strip()]
+
+
+def parse_bool_list(text: str) -> list[bool]:
+    out: list[bool] = []
+    for item in text.split(","):
+        key = item.strip().lower()
+        if not key:
+            continue
+        if key in {"1", "true", "on", "yes"}:
+            out.append(True)
+        elif key in {"0", "false", "off", "no"}:
+            out.append(False)
+        else:
+            raise argparse.ArgumentTypeError(f"Invalid bool item: {item}")
+    return out
+
+
+def make_sim_args(args: argparse.Namespace, trial: dict[str, float | str], cfg: dict[str, Any]) -> argparse.Namespace:
+    argv = [
+        "ik_slalom_sim2sim.py",
+        "--test",
+        str(trial["name"]),
+        "--duration",
+        str(args.duration),
+        "--settle",
+        str(args.settle),
+        "--speed",
+        str(trial["speed"]),
+        "--yaw-rate",
+        str(trial["yaw"]),
+        "--posture",
+        "custom",
+        "--custom-abduction",
+        str(args.custom_abduction),
+        "--custom-hip",
+        str(args.custom_hip),
+        "--custom-knee",
+        str(args.custom_knee),
+        "--wheel-model",
+        "direct",
+        "--linear-wheel-gain",
+        str(args.linear_wheel_gain),
+        "--direct-yaw-wheel-gain",
+        str(args.direct_yaw_wheel_gain),
+        "--max-wheel-speed",
+        str(args.max_wheel_speed),
+        "--wheel-accel-limit",
+        str(args.wheel_accel_limit),
+        "--yaw-rate-kp",
+        str(cfg["yaw_rate_kp"]),
+        "--encoder-posture-kp",
+        str(cfg["encoder_posture_kp"]),
+        "--encoder-posture-max",
+        str(cfg["encoder_posture_max"]),
+        "--roll-comp-gain",
+        str(cfg["roll_comp_gain"]),
+        "--pitch-comp-gain",
+        str(cfg["pitch_comp_gain"]),
+        "--no-realtime",
+    ]
+    argv.append("--imu-posture" if cfg["imu_posture"] else "--no-imu-posture")
+    argv.append("--encoder-guard" if cfg["encoder_guard"] else "--no-encoder-guard")
+    argv.append("--imu-guard" if cfg["imu_guard"] else "--no-imu-guard")
+    old_argv = sys.argv
+    try:
+        sys.argv = argv
+        return sim.parse_args()
+    finally:
+        sys.argv = old_argv
+
+
+def score_trial(out: dict[str, Any], trial: dict[str, float | str]) -> dict[str, float]:
+    vx = float(out["mean_body_vx_mps"])
+    yaw = float(out["mean_yaw_rate_rad_s"])
+    vx_err = abs(vx - float(trial["target_vx"]))
+    yaw_err = abs(yaw - float(trial["target_yaw"]))
+    return {
+        "vx": vx,
+        "yaw": yaw,
+        "imu_gyro_z": float(out["mean_imu_gyro_z_rad_s"]),
+        "vx_err": vx_err,
+        "yaw_err": yaw_err,
+        "err": vx_err + yaw_err,
+    }
+
+
+def run_sweep(args: argparse.Namespace) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for imu_posture in args.imu_posture_values:
+        for encoder_guard in args.encoder_guard_values:
+            for imu_guard in args.imu_guard_values:
+                for encoder_posture_kp in args.encoder_posture_kps:
+                    for encoder_posture_max in args.encoder_posture_maxs:
+                        for yaw_rate_kp in args.yaw_rate_kps:
+                            for roll_comp_gain in args.roll_comp_gains:
+                                for pitch_comp_gain in args.pitch_comp_gains:
+                                    cfg = {
+                                        "imu_posture": imu_posture,
+                                        "encoder_guard": encoder_guard,
+                                        "imu_guard": imu_guard,
+                                        "encoder_posture_kp": encoder_posture_kp,
+                                        "encoder_posture_max": encoder_posture_max,
+                                        "yaw_rate_kp": yaw_rate_kp,
+                                        "roll_comp_gain": roll_comp_gain,
+                                        "pitch_comp_gain": pitch_comp_gain,
+                                    }
+                                    detail: list[dict[str, Any]] = []
+                                    speed_error = 0.0
+                                    max_tilt = 0.0
+                                    max_leg = 0.0
+                                    mean_wheel_err = 0.0
+                                    stable_all = True
+                                    for trial in TRIALS:
+                                        sim_args = make_sim_args(args, trial, cfg)
+                                        out = sim.run_one(str(trial["name"]), sim_args)
+                                        trial_score = score_trial(out, trial)
+                                        trial_score["test"] = str(trial["name"])
+                                        detail.append(trial_score)
+                                        speed_error += trial_score["err"]
+                                        max_tilt = max(max_tilt, float(out["max_tilt_deg"]))
+                                        max_leg = max(max_leg, float(out["max_leg_encoder_error_rad"]))
+                                        mean_wheel_err += float(out["mean_wheel_speed_error_rad_s"])
+                                        stable_all = stable_all and bool(out["stable"])
+
+                                    score = (
+                                        speed_error
+                                        + args.tilt_weight * max_tilt
+                                        + args.leg_error_weight * max_leg
+                                        + args.wheel_error_weight * (mean_wheel_err / len(TRIALS))
+                                    )
+                                    row = {
+                                        **cfg,
+                                        "score": round(score, 6),
+                                        "speed_error_sum": round(speed_error, 6),
+                                        "max_tilt_deg": round(max_tilt, 5),
+                                        "max_leg_encoder_error_rad": round(max_leg, 6),
+                                        "mean_wheel_speed_error_rad_s": round(mean_wheel_err / len(TRIALS), 6),
+                                        "stable_all": stable_all,
+                                        "detail": detail,
+                                    }
+                                    rows.append(row)
+                                    print(
+                                        "DONE "
+                                        + json.dumps(
+                                            {k: v for k, v in row.items() if k != "detail"},
+                                            ensure_ascii=False,
+                                        ),
+                                        flush=True,
+                                    )
+    rows.sort(key=lambda r: float(r["score"]))
+    return rows
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--duration", type=float, default=3.0)
+    parser.add_argument("--settle", type=float, default=1.5)
+    parser.add_argument("--custom-abduction", type=float, default=0.2)
+    parser.add_argument("--custom-hip", type=float, default=1.697)
+    parser.add_argument("--custom-knee", type=float, default=-2.650)
+    parser.add_argument("--linear-wheel-gain", type=float, default=12.5)
+    parser.add_argument("--direct-yaw-wheel-gain", type=float, default=8.0)
+    parser.add_argument("--max-wheel-speed", type=float, default=12.0)
+    parser.add_argument("--wheel-accel-limit", type=float, default=35.0)
+    parser.add_argument("--imu-posture-values", type=parse_bool_list, default=[True, False])
+    parser.add_argument("--encoder-guard-values", type=parse_bool_list, default=[True])
+    parser.add_argument("--imu-guard-values", type=parse_bool_list, default=[True])
+    parser.add_argument("--encoder-posture-kps", type=parse_float_list, default=[0.0, 0.05, 0.15, 0.30])
+    parser.add_argument("--encoder-posture-maxs", type=parse_float_list, default=[0.03])
+    parser.add_argument("--yaw-rate-kps", type=parse_float_list, default=[0.0, 0.4, 0.8])
+    parser.add_argument("--roll-comp-gains", type=parse_float_list, default=[0.35])
+    parser.add_argument("--pitch-comp-gains", type=parse_float_list, default=[0.35])
+    parser.add_argument("--tilt-weight", type=float, default=0.02)
+    parser.add_argument("--leg-error-weight", type=float, default=0.5)
+    parser.add_argument("--wheel-error-weight", type=float, default=0.0)
+    parser.add_argument("--top", type=int, default=12)
+    parser.add_argument("--json", type=Path, default=None)
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    rows = run_sweep(args)
+    if args.json:
+        args.json.write_text(json.dumps(rows, indent=2), encoding="utf-8")
+
+    print("\nTop compensation parameter sets")
+    print("rank score speed_err tilt leg_err wheel_err imu enc_kp yaw_kp enc_guard imu_guard")
+    for i, row in enumerate(rows[: args.top], 1):
+        print(
+            f"{i:2d} {row['score']:7.4f} {row['speed_error_sum']:7.4f} "
+            f"{row['max_tilt_deg']:5.2f} {row['max_leg_encoder_error_rad']:7.4f} "
+            f"{row['mean_wheel_speed_error_rad_s']:7.4f} "
+            f"{int(row['imu_posture'])} {row['encoder_posture_kp']:6.3f} "
+            f"{row['yaw_rate_kp']:6.3f} {int(row['encoder_guard'])} {int(row['imu_guard'])}"
+        )
+        for d in row["detail"]:
+            print(f"   {d['test']:<7} vx={d['vx']:+.3f} yaw={d['yaw']:+.3f} err={d['err']:.3f}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/05_software/train/rc_mjlab/sim2sim/ik_slalom_sim2sim.py b/05_software/train/rc_mjlab/sim2sim/ik_slalom_sim2sim.py
new file mode 100644
index 0000000..5107681
--- /dev/null
+++ b/05_software/train/rc_mjlab/sim2sim/ik_slalom_sim2sim.py
@@ -0,0 +1,1235 @@
+#!/usr/bin/env python3
+"""
+Standalone sim2sim check for wheel-leg IK navigation.
+
+This file is intentionally isolated from nav_sim2sim.py and the ROS2 runtime.
+It keeps the 12 leg joints at the RL rough-terrain default pose and drives the
+four wheel velocity actuators with a differential-drive inverse kinematics law.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import sys
+import tempfile
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Iterable
+
+import mujoco
+import mujoco.viewer
+import numpy as np
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+SIM2SIM_DIR = PROJECT_ROOT / "sim2sim"
+if str(SIM2SIM_DIR) not in sys.path:
+    sys.path.insert(0, str(SIM2SIM_DIR))
+
+from tools.math_utils import get_gravity_orientation  # noqa: E402
+
+
+LEG_JOINT_NAMES = [
+    "fl_hip_abduction_joint",
+    "fl_hip_pitch_joint",
+    "fl_knee_joint",
+    "fr_hip_abduction_joint",
+    "fr_hip_pitch_joint",
+    "fr_knee_joint",
+    "rl_hip_abduction_joint",
+    "rl_hip_pitch_joint",
+    "rl_knee_joint",
+    "rr_hip_abduction_joint",
+    "rr_hip_pitch_joint",
+    "rr_knee_joint",
+]
+WHEEL_JOINT_NAMES = [
+    "fl_wheel_joint",
+    "fr_wheel_joint",
+    "rl_wheel_joint",
+    "rr_wheel_joint",
+]
+ALL_JOINT_NAMES = LEG_JOINT_NAMES + WHEEL_JOINT_NAMES
+WHEEL_BODY_NAMES = [
+    "fl_wheel_Link",
+    "fr_wheel_Link",
+    "rl_wheel_Link",
+    "rr_wheel_Link",
+]
+
+# Must match src/robot/robot_cfg.py rough policy initial state.
+ROUGH_DEFAULT_DOF_POS = np.array(
+    [0.0, 0.550, -1.125] * 4 + [0.0] * 4,
+    dtype=np.float64,
+)
+
+# From mujoco_sim/config.py. These poses keep the wheel contact close to the
+# hip in sagittal X while changing body height.
+HEIGHT_TABLE = np.array(
+    [
+        (0.17, 0.914, -2.628),
+        (0.19, 0.926, -2.528),
+        (0.21, 0.924, -2.428),
+        (0.23, 0.912, -2.328),
+        (0.25, 0.892, -2.226),
+        (0.27, 0.864, -2.122),
+        (0.29, 0.834, -2.014),
+        (0.31, 0.798, -1.906),
+        (0.33, 0.758, -1.792),
+        (0.35, 0.714, -1.672),
+        (0.37, 0.666, -1.546),
+        (0.39, 0.612, -1.412),
+        (0.41, 0.552, -1.266),
+        (0.43, 0.484, -1.104),
+        (0.45, 0.404, -0.918),
+    ],
+    dtype=np.float64,
+)
+
+
+def wrap_pi(x: float) -> float:
+    return (x + math.pi) % (2.0 * math.pi) - math.pi
+
+
+def quat_to_euler_wxyz(q: np.ndarray) -> tuple[float, float, float]:
+    w, x, y, z = q
+    sinr_cosp = 2.0 * (w * x + y * z)
+    cosr_cosp = 1.0 - 2.0 * (x * x + y * y)
+    roll = math.atan2(sinr_cosp, cosr_cosp)
+
+    sinp = 2.0 * (w * y - z * x)
+    pitch = math.asin(float(np.clip(sinp, -1.0, 1.0)))
+
+    siny_cosp = 2.0 * (w * z + x * y)
+    cosy_cosp = 1.0 - 2.0 * (y * y + z * z)
+    yaw = math.atan2(siny_cosp, cosy_cosp)
+    return roll, pitch, yaw
+
+
+def yaw_to_quat_wxyz(yaw: float) -> np.ndarray:
+    half = 0.5 * yaw
+    return np.array([math.cos(half), 0.0, 0.0, math.sin(half)], dtype=np.float64)
+
+
+def quat_to_mat_wxyz(q: np.ndarray) -> np.ndarray:
+    w, x, y, z = q
+    return np.array(
+        [
+            [1.0 - 2.0 * (y * y + z * z), 2.0 * (x * y - z * w), 2.0 * (x * z + y * w)],
+            [2.0 * (x * y + z * w), 1.0 - 2.0 * (x * x + z * z), 2.0 * (y * z - x * w)],
+            [2.0 * (x * z - y * w), 2.0 * (y * z + x * w), 1.0 - 2.0 * (x * x + y * y)],
+        ],
+        dtype=np.float64,
+    )
+
+
+def table_pose_for_height(height: float) -> np.ndarray:
+    h = HEIGHT_TABLE[:, 0]
+    hip = float(np.interp(np.clip(height, h[0], h[-1]), h, HEIGHT_TABLE[:, 1]))
+    knee = float(np.interp(np.clip(height, h[0], h[-1]), h, HEIGHT_TABLE[:, 2]))
+    return np.array([0.0, hip, knee] * 4 + [0.0] * 4, dtype=np.float64)
+
+
+def nominal_dof_pos(args: argparse.Namespace) -> np.ndarray:
+    if args.posture == "custom":
+        ab = args.custom_abduction
+        return np.array(
+            [
+                ab,
+                args.custom_hip,
+                args.custom_knee,
+                -ab,
+                args.custom_hip,
+                args.custom_knee,
+                ab,
+                args.custom_hip,
+                args.custom_knee,
+                -ab,
+                args.custom_hip,
+                args.custom_knee,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            dtype=np.float64,
+        )
+    if args.posture == "table":
+        return table_pose_for_height(args.body_height)
+    return ROUGH_DEFAULT_DOF_POS.copy()
+
+
+def make_flat_scene_xml(robot_abs: str, tmp_dir: Path) -> Path:
+    out = tmp_dir / "ik_flat_scene.xml"
+    out.write_text(
+        f"""<mujoco model="wheelleg_flat_scene">
+  <include file="{robot_abs}"/>
+
+  <option timestep="0.002" gravity="0 0 -9.81" integrator="implicitfast"/>
+  <statistic center="0 0 0.35" extent="3.0"/>
+
+  <visual>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3"/>
+    <global azimuth="120" elevation="-20"/>
+  </visual>
+
+  <asset>
+    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" mark="edge"
+      rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3" markrgb="0.8 0.8 0.8" width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+  </asset>
+
+  <worldbody>
+    <light pos="0 0 3" dir="0 0 -1" directional="true"/>
+    <geom name="floor" size="0 0 0.05" type="plane" material="groundplane" friction="0.8 0.05 0.01"/>
+  </worldbody>
+</mujoco>
+""",
+        encoding="utf-8",
+    )
+    return out
+
+
+def make_sim_xml(terrain_xml: Path | None, robot_xml: Path, tmp_dir: Path) -> Path:
+    robot_content = robot_xml.read_text(encoding="utf-8")
+    mesh_dir = str((robot_xml.parent / "meshes").resolve()).replace("\\", "/")
+    robot_content = robot_content.replace(
+        '<compiler angle="radian" meshdir="meshes/"/>',
+        f'<compiler angle="radian" meshdir="{mesh_dir}"/>',
+    )
+    tmp_dir.mkdir(parents=True, exist_ok=True)
+    robot_out = tmp_dir / "wheelleg_abs_mesh.xml"
+    robot_out.write_text(robot_content, encoding="utf-8")
+
+    robot_abs = str(robot_out.resolve()).replace("\\", "/")
+    if terrain_xml is None:
+        return make_flat_scene_xml(robot_abs, tmp_dir)
+
+    content = terrain_xml.read_text(encoding="utf-8")
+    content = content.replace('<include file="go2w.xml"/>', f'<include file="{robot_abs}"/>')
+    content = content.replace('<include file="wheelleg.xml"/>', f'<include file="{robot_abs}"/>')
+
+    terrain_dir = terrain_xml.parent
+    replacements = {
+        "../height_field.png": str((terrain_dir / "height_field.png").resolve()).replace("\\", "/"),
+        "../unitree_hfield.png": str((terrain_dir / "unitree_hfield.png").resolve()).replace("\\", "/"),
+    }
+    for old, new in replacements.items():
+        content = content.replace(old, new)
+
+    out = tmp_dir / "ik_slalom_scene.xml"
+    out.write_text(content, encoding="utf-8")
+    return out
+
+
+def rebuild_actuators(spec: mujoco.MjSpec) -> None:
+    for actuator in list(spec.actuators):
+        spec.delete(actuator)
+
+    kp_leg = 50.0
+    kd_leg = 1.5
+    kd_wheel = 1.0
+    effort_limit = 17.0
+
+    for jname in LEG_JOINT_NAMES:
+        act = spec.add_actuator(name=jname, target=jname)
+        act.trntype = mujoco.mjtTrn.mjTRN_JOINT
+        act.dyntype = mujoco.mjtDyn.mjDYN_NONE
+        act.gaintype = mujoco.mjtGain.mjGAIN_FIXED
+        act.biastype = mujoco.mjtBias.mjBIAS_AFFINE
+        act.gainprm[0] = kp_leg
+        act.biasprm[1] = -kp_leg
+        act.biasprm[2] = -kd_leg
+        act.forcelimited = True
+        act.forcerange[:] = [-effort_limit, effort_limit]
+        act.ctrllimited = False
+
+    for jname in WHEEL_JOINT_NAMES:
+        act = spec.add_actuator(name=jname, target=jname)
+        act.trntype = mujoco.mjtTrn.mjTRN_JOINT
+        act.dyntype = mujoco.mjtDyn.mjDYN_NONE
+        act.gaintype = mujoco.mjtGain.mjGAIN_FIXED
+        act.biastype = mujoco.mjtBias.mjBIAS_AFFINE
+        act.gainprm[0] = kd_wheel
+        act.biasprm[2] = -kd_wheel
+        act.forcelimited = True
+        act.forcerange[:] = [-effort_limit, effort_limit]
+        act.ctrllimited = False
+
+
+@dataclass(frozen=True)
+class ModelIds:
+    qpos: np.ndarray
+    qvel: np.ndarray
+    ctrl: np.ndarray
+    wheel_body: np.ndarray
+    wheel_geom: np.ndarray
+
+
+@dataclass
+class SensorFrame:
+    time: float
+    base_pos: np.ndarray
+    quat_wxyz: np.ndarray
+    rpy: np.ndarray
+    gyro: np.ndarray
+    projected_gravity: np.ndarray
+    joint_pos: np.ndarray
+    joint_vel: np.ndarray
+    wheel_vel: np.ndarray
+
+
+def name_id(model: mujoco.MjModel, obj_type: mujoco.mjtObj, name: str) -> int:
+    idx = mujoco.mj_name2id(model, obj_type, name)
+    if idx < 0:
+        raise RuntimeError(f"MuJoCo object not found: {name}")
+    return idx
+
+
+def build_model(args: argparse.Namespace, tmp_dir: Path) -> tuple[mujoco.MjModel, mujoco.MjData, ModelIds]:
+    scene_xml = make_sim_xml(args.terrain_xml, args.robot_xml, tmp_dir)
+
+    old_cwd = Path.cwd()
+    os.chdir(str(args.robot_xml.parent))
+    try:
+        spec = mujoco.MjSpec.from_file(str(scene_xml))
+        rebuild_actuators(spec)
+        model = spec.compile()
+    finally:
+        os.chdir(str(old_cwd))
+
+    data = mujoco.MjData(model)
+    qpos_ids = np.array(
+        [model.jnt_qposadr[name_id(model, mujoco.mjtObj.mjOBJ_JOINT, n)] for n in ALL_JOINT_NAMES],
+        dtype=np.int32,
+    )
+    qvel_ids = np.array(
+        [model.jnt_dofadr[name_id(model, mujoco.mjtObj.mjOBJ_JOINT, n)] for n in ALL_JOINT_NAMES],
+        dtype=np.int32,
+    )
+    ctrl_ids = np.array(
+        [name_id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, n) for n in ALL_JOINT_NAMES],
+        dtype=np.int32,
+    )
+    wheel_body_ids = np.array(
+        [name_id(model, mujoco.mjtObj.mjOBJ_BODY, n) for n in WHEEL_BODY_NAMES],
+        dtype=np.int32,
+    )
+
+    wheel_geom_ids: list[int] = []
+    for body_id in wheel_body_ids:
+        for geom_id in range(model.ngeom):
+            if model.geom_bodyid[geom_id] == body_id and model.geom_type[geom_id] == mujoco.mjtGeom.mjGEOM_CYLINDER:
+                wheel_geom_ids.append(geom_id)
+                break
+        else:
+            raise RuntimeError(f"No cylinder collision geom found for body id {body_id}")
+
+    return model, data, ModelIds(qpos_ids, qvel_ids, ctrl_ids, wheel_body_ids, np.array(wheel_geom_ids))
+
+
+def reset_robot(model: mujoco.MjModel, data: mujoco.MjData, ids: ModelIds, args: argparse.Namespace) -> np.ndarray:
+    default_dof_pos = nominal_dof_pos(args)
+    data.qpos[:3] = [args.start_x, args.start_y, args.start_z]
+    data.qpos[3:7] = [1.0, 0.0, 0.0, 0.0]
+    data.qpos[ids.qpos] = default_dof_pos
+    data.qvel[:] = 0.0
+    data.ctrl[ids.ctrl[:12]] = default_dof_pos[:12]
+    data.ctrl[ids.ctrl[12:]] = 0.0
+    mujoco.mj_forward(model, data)
+
+    settle_steps = int(round(args.settle / model.opt.timestep))
+    for _ in range(settle_steps):
+        data.ctrl[ids.ctrl[:12]] = default_dof_pos[:12]
+        data.ctrl[ids.ctrl[12:]] = 0.0
+        mujoco.mj_step(model, data)
+    return default_dof_pos
+
+
+def read_sensors(model: mujoco.MjModel, data: mujoco.MjData, ids: ModelIds) -> SensorFrame:
+    del model
+    quat = data.qpos[3:7].copy()
+    rpy = np.array(quat_to_euler_wxyz(quat), dtype=np.float64)
+    joint_pos = data.qpos[ids.qpos].copy()
+    joint_vel = data.qvel[ids.qvel].copy()
+    return SensorFrame(
+        time=float(data.time),
+        base_pos=data.qpos[:3].copy(),
+        quat_wxyz=quat,
+        rpy=rpy,
+        gyro=data.qvel[3:6].copy(),
+        projected_gravity=get_gravity_orientation(quat),
+        joint_pos=joint_pos,
+        joint_vel=joint_vel,
+        wheel_vel=joint_vel[12:].copy(),
+    )
+
+
+@dataclass(frozen=True)
+class WheelGeometry:
+    radius: float
+    track_width: float
+    wheel_y: np.ndarray
+
+
+def infer_wheel_geometry(model: mujoco.MjModel, data: mujoco.MjData, ids: ModelIds) -> WheelGeometry:
+    radius_values = model.geom_size[ids.wheel_geom, 0]
+    radius = float(np.mean(radius_values))
+    wheel_y = data.xipos[ids.wheel_body, 1].copy()
+    left_y = wheel_y[[0, 2]]
+    right_y = wheel_y[[1, 3]]
+    track = float(np.mean(left_y) - np.mean(right_y))
+    if radius <= 0.0 or track <= 0.0:
+        raise RuntimeError(f"Invalid wheel geometry: radius={radius}, track={track}")
+    return WheelGeometry(radius=radius, track_width=track, wheel_y=wheel_y)
+
+
+@dataclass
+class IkCommand:
+    linear_x: float
+    yaw_rate: float
+    target_index: int = -1
+    target_distance: float = 0.0
+
+
+class DifferentialWheelIk:
+    def __init__(
+        self,
+        geometry: WheelGeometry,
+        max_wheel_speed: float,
+        wheel_signs: np.ndarray,
+        yaw_wheel_gain: float,
+        args: argparse.Namespace,
+    ) -> None:
+        self.geometry = geometry
+        self.max_wheel_speed = max_wheel_speed
+        self.wheel_signs = wheel_signs.astype(np.float64)
+        self.yaw_wheel_gain = yaw_wheel_gain
+        self.args = args
+
+    def solve(self, cmd: IkCommand, sensor: SensorFrame | None = None) -> np.ndarray:
+        half_track = 0.5 * self.geometry.track_width
+        yaw_rate = cmd.yaw_rate
+        if sensor is not None:
+            yaw_rate += self.args.yaw_rate_kp * (cmd.yaw_rate - float(sensor.gyro[2]))
+
+        if self.args.wheel_model == "direct":
+            linear_wheel = self.args.linear_wheel_gain * cmd.linear_x
+            yaw_wheel = self.args.direct_yaw_wheel_gain * yaw_rate
+        else:
+            linear_wheel = cmd.linear_x / self.geometry.radius
+            yaw_wheel = self.yaw_wheel_gain * yaw_rate * half_track / self.geometry.radius
+            yaw_wheel = float(np.clip(yaw_wheel, -self.args.max_yaw_wheel_speed, self.args.max_yaw_wheel_speed))
+        left = linear_wheel - yaw_wheel
+        right = linear_wheel + yaw_wheel
+        wheel = np.array([left, right, left, right], dtype=np.float64)
+        wheel = np.clip(wheel, -self.max_wheel_speed, self.max_wheel_speed)
+        return wheel * self.wheel_signs
+
+
+class LegStanceIk:
+    """Numerical wheel-center IK for keeping the rough RL stance level."""
+
+    def __init__(self, model: mujoco.MjModel, data: mujoco.MjData, ids: ModelIds, args: argparse.Namespace) -> None:
+        self.model = model
+        self.ids = ids
+        self.args = args
+        self.shadow = mujoco.MjData(model)
+        self.jacp = np.zeros((3, model.nv), dtype=np.float64)
+        self.jacr = np.zeros((3, model.nv), dtype=np.float64)
+        self.last_target = ROUGH_DEFAULT_DOF_POS[:12].copy()
+
+        self.leg_qpos = [ids.qpos[i * 3 : i * 3 + 3] for i in range(4)]
+        self.leg_qvel = [ids.qvel[i * 3 : i * 3 + 3] for i in range(4)]
+        self.leg_joint_ids = [
+            [name_id(model, mujoco.mjtObj.mjOBJ_JOINT, LEG_JOINT_NAMES[i * 3 + j]) for j in range(3)]
+            for i in range(4)
+        ]
+        self.joint_ranges = [
+            np.array([model.jnt_range[jid].copy() for jid in leg], dtype=np.float64)
+            for leg in self.leg_joint_ids
+        ]
+
+        mujoco.mj_forward(model, data)
+        base_pos = data.qpos[:3].copy()
+        base_rot = quat_to_mat_wxyz(data.qpos[3:7])
+        self.nominal_body_wheel = (base_rot.T @ (data.xipos[ids.wheel_body] - base_pos).T).T
+
+    def compute(self, data: mujoco.MjData, sensor: SensorFrame) -> np.ndarray:
+        current_rot = quat_to_mat_wxyz(sensor.quat_wxyz)
+        level_rot = quat_to_mat_wxyz(yaw_to_quat_wxyz(float(sensor.rpy[2])))
+        base_targets = (current_rot @ self.nominal_body_wheel.T).T
+        level_targets = (level_rot @ self.nominal_body_wheel.T).T
+        target_world = sensor.base_pos + base_targets + self.args.posture_gain * (level_targets - base_targets)
+
+        self.shadow.qpos[:] = data.qpos
+        self.shadow.qvel[:] = 0.0
+        mujoco.mj_forward(self.model, self.shadow)
+
+        damping = self.args.leg_ik_damping
+        for _ in range(self.args.leg_ik_iterations):
+            for leg_idx, body_id in enumerate(self.ids.wheel_body):
+                err = target_world[leg_idx] - self.shadow.xipos[body_id]
+                if float(np.linalg.norm(err)) < 1e-4:
+                    continue
+
+                self.jacp.fill(0.0)
+                self.jacr.fill(0.0)
+                mujoco.mj_jacBody(self.model, self.shadow, self.jacp, self.jacr, int(body_id))
+                cols = self.leg_qvel[leg_idx]
+                j_leg = self.jacp[:, cols]
+                lhs = j_leg @ j_leg.T + (damping * damping) * np.eye(3)
+                dq = j_leg.T @ np.linalg.solve(lhs, err)
+                dq = np.clip(dq, -self.args.leg_ik_step, self.args.leg_ik_step)
+
+                qpos_ids = self.leg_qpos[leg_idx]
+                self.shadow.qpos[qpos_ids] += dq
+                ranges = self.joint_ranges[leg_idx]
+                self.shadow.qpos[qpos_ids] = np.clip(self.shadow.qpos[qpos_ids], ranges[:, 0], ranges[:, 1])
+            mujoco.mj_forward(self.model, self.shadow)
+
+        raw_target = self.shadow.qpos[self.ids.qpos[:12]].copy()
+        offset = np.clip(
+            raw_target - ROUGH_DEFAULT_DOF_POS[:12],
+            -self.args.max_leg_offset,
+            self.args.max_leg_offset,
+        )
+        raw_target = ROUGH_DEFAULT_DOF_POS[:12] + offset
+
+        max_delta = self.args.leg_target_rate
+        filtered = self.last_target + np.clip(raw_target - self.last_target, -max_delta, max_delta)
+        alpha = self.args.leg_target_filter
+        filtered = (1.0 - alpha) * self.last_target + alpha * filtered
+        self.last_target = filtered.copy()
+        return filtered
+
+
+class TablePostureController:
+    def __init__(self, args: argparse.Namespace, initial_target: np.ndarray) -> None:
+        self.args = args
+        self.target = initial_target[:12].copy()
+
+    def compute(self, sensor: SensorFrame) -> np.ndarray:
+        if self.args.posture == "rough":
+            raw = ROUGH_DEFAULT_DOF_POS[:12].copy()
+        elif self.args.posture == "custom":
+            raw = nominal_dof_pos(self.args)[:12].copy()
+        else:
+            pose = table_pose_for_height(self.args.body_height)
+            raw = pose[:12].copy()
+
+        if self.args.imu_posture:
+            roll_corr = -self.args.roll_comp_gain * float(sensor.rpy[0])
+            pitch_corr = -self.args.pitch_comp_gain * float(sensor.rpy[1])
+            for i, side in enumerate((1.0, -1.0, 1.0, -1.0)):
+                raw[i * 3] = np.clip(raw[i * 3] + side * roll_corr, -0.5, 0.5)
+                raw[i * 3 + 1] = np.clip(raw[i * 3 + 1] + pitch_corr, -1.0, 2.5)
+                raw[i * 3 + 2] = np.clip(raw[i * 3 + 2], -2.65, -0.3)
+
+        if self.args.encoder_posture_kp > 0.0:
+            encoder_err = self.target - sensor.joint_pos[:12]
+            raw += np.clip(
+                self.args.encoder_posture_kp * encoder_err,
+                -self.args.encoder_posture_max,
+                self.args.encoder_posture_max,
+            )
+
+        max_delta = self.args.leg_target_rate
+        raw = self.target + np.clip(raw - self.target, -max_delta, max_delta)
+        alpha = self.args.leg_target_filter
+        self.target = (1.0 - alpha) * self.target + alpha * raw
+        return self.target.copy()
+
+
+def sensor_command_scale(sensor: SensorFrame, leg_target: np.ndarray, args: argparse.Namespace) -> float:
+    scale = 1.0
+    if args.encoder_guard:
+        leg_error = float(np.max(np.abs(sensor.joint_pos[:12] - leg_target)))
+        if leg_error >= args.encoder_guard_stop:
+            scale = 0.0
+        elif leg_error > args.encoder_guard_start:
+            span = max(1e-6, args.encoder_guard_stop - args.encoder_guard_start)
+            scale *= 1.0 - (leg_error - args.encoder_guard_start) / span
+
+    if args.imu_guard:
+        tilt = math.hypot(float(sensor.rpy[0]), float(sensor.rpy[1]))
+        start = math.radians(args.imu_guard_start_deg)
+        stop = math.radians(args.imu_guard_stop_deg)
+        if tilt >= stop:
+            scale = 0.0
+        elif tilt > start:
+            scale *= 1.0 - (tilt - start) / max(1e-6, stop - start)
+
+    return float(np.clip(scale, 0.0, 1.0))
+
+
+class PathFollower:
+    def __init__(
+        self,
+        waypoints: Iterable[tuple[float, float]],
+        max_speed: float,
+        max_yaw_rate: float,
+        arrive_radius: float,
+        allow_reverse: bool,
+    ) -> None:
+        self.waypoints = list(waypoints)
+        self.idx = 0
+        self.max_speed = max_speed
+        self.max_yaw_rate = max_yaw_rate
+        self.arrive_radius = arrive_radius
+        self.allow_reverse = allow_reverse
+
+    @property
+    def done(self) -> bool:
+        return self.idx >= len(self.waypoints)
+
+    def update(self, sensor: SensorFrame) -> IkCommand:
+        if self.done:
+            return IkCommand(0.0, 0.0, self.idx, 0.0)
+
+        x, y = sensor.base_pos[:2]
+        target = self.waypoints[self.idx]
+        dx = target[0] - float(x)
+        dy = target[1] - float(y)
+        dist = math.hypot(dx, dy)
+        while dist < self.arrive_radius and self.idx < len(self.waypoints) - 1:
+            self.idx += 1
+            target = self.waypoints[self.idx]
+            dx = target[0] - float(x)
+            dy = target[1] - float(y)
+            dist = math.hypot(dx, dy)
+        if dist < self.arrive_radius and self.idx == len(self.waypoints) - 1:
+            self.idx = len(self.waypoints)
+            return IkCommand(0.0, 0.0, self.idx, dist)
+
+        desired_yaw = math.atan2(dy, dx)
+        yaw_error = wrap_pi(desired_yaw - float(sensor.rpy[2]))
+        direction = 1.0
+        if self.allow_reverse and abs(yaw_error) > math.pi * 0.5:
+            desired_yaw = wrap_pi(desired_yaw + math.pi)
+            yaw_error = wrap_pi(desired_yaw - float(sensor.rpy[2]))
+            direction = -1.0
+        yaw_rate = float(np.clip(2.8 * yaw_error, -self.max_yaw_rate, self.max_yaw_rate))
+
+        speed = min(self.max_speed, 1.2 * dist)
+        speed *= max(0.15, math.cos(min(abs(yaw_error), math.pi * 0.5)))
+        speed *= direction
+        return IkCommand(speed, yaw_rate, self.idx, dist)
+
+
+class PurePursuitFollower:
+    def __init__(self, waypoints: Iterable[tuple[float, float]], args: argparse.Namespace) -> None:
+        self.points = np.array(list(waypoints), dtype=np.float64)
+        self.args = args
+        self.progress = 0
+        self.closest_distance = float("inf")
+        self.finished = False
+        if len(self.points) < 2:
+            raise ValueError("PurePursuitFollower needs at least two points")
+        seg = self.points[1:] - self.points[:-1]
+        self.seg_len = np.linalg.norm(seg, axis=1)
+        self.cum = np.concatenate([[0.0], np.cumsum(self.seg_len)])
+        self.total_len = float(self.cum[-1])
+
+    @property
+    def done(self) -> bool:
+        return self.finished
+
+    @property
+    def idx(self) -> int:
+        return int(self.progress)
+
+    def _project_s(self, p: np.ndarray) -> tuple[float, float]:
+        best_s = 0.0
+        best_d = float("inf")
+        start_i = max(0, self.progress - 1)
+        end_i = min(len(self.points) - 1, self.progress + self.args.pure_search_segments)
+        for i in range(start_i, end_i):
+            a = self.points[i]
+            b = self.points[i + 1]
+            ab = b - a
+            denom = float(np.dot(ab, ab))
+            if denom < 1e-9:
+                continue
+            t = float(np.clip(np.dot(p - a, ab) / denom, 0.0, 1.0))
+            proj = a + t * ab
+            d = float(np.linalg.norm(p - proj))
+            if d < best_d:
+                best_d = d
+                best_s = float(self.cum[i] + t * self.seg_len[i])
+                self.progress = max(self.progress, i)
+        return best_s, best_d
+
+    def _point_at_s(self, s: float) -> np.ndarray:
+        s = float(np.clip(s, 0.0, self.total_len))
+        i = int(np.searchsorted(self.cum, s, side="right") - 1)
+        i = int(np.clip(i, 0, len(self.seg_len) - 1))
+        local = 0.0 if self.seg_len[i] < 1e-9 else (s - self.cum[i]) / self.seg_len[i]
+        return self.points[i] + local * (self.points[i + 1] - self.points[i])
+
+    def update(self, sensor: SensorFrame) -> IkCommand:
+        p = sensor.base_pos[:2].astype(np.float64)
+        s, dist_path = self._project_s(p)
+        self.closest_distance = min(self.closest_distance, dist_path)
+        goal_dist = float(np.linalg.norm(self.points[-1] - p))
+        if self.total_len - s < self.args.arrive_radius and goal_dist < self.args.arrive_radius:
+            self.finished = True
+            return IkCommand(0.0, 0.0, len(self.points) - 1, goal_dist)
+
+        lookahead = np.clip(
+            self.args.lookahead_base + self.args.lookahead_time * abs(self.args.speed),
+            self.args.lookahead_min,
+            self.args.lookahead_max,
+        )
+        target = self._point_at_s(s + lookahead)
+        dx, dy = target - p
+        yaw = float(sensor.rpy[2])
+        c, sn = math.cos(yaw), math.sin(yaw)
+        x_body = c * dx + sn * dy
+        y_body = -sn * dx + c * dy
+        ld2 = max(lookahead * lookahead, 1e-6)
+        curvature = 2.0 * y_body / ld2
+
+        abs_curv = abs(curvature)
+        if abs_curv > 1e-6:
+            v_curve = math.sqrt(max(self.args.max_lat_acc, 1e-6) / abs_curv)
+        else:
+            v_curve = self.args.speed
+        speed = min(self.args.speed, v_curve)
+        speed = max(self.args.min_path_speed, speed)
+        if x_body < -0.05 and self.args.allow_reverse:
+            speed = -speed
+        yaw_rate = float(np.clip(speed * curvature, -self.args.max_yaw_rate, self.args.max_yaw_rate))
+        return IkCommand(float(speed), yaw_rate, self.progress, dist_path)
+
+
+def args_sign(x: float) -> float:
+    return -1.0 if x < -0.05 else 1.0
+
+
+def test_command(test_name: str, elapsed: float, sensor: SensorFrame, follower: PathFollower | None, args: argparse.Namespace) -> IkCommand:
+    if test_name == "forward":
+        return IkCommand(args.speed, 0.0)
+    if test_name == "yaw":
+        return IkCommand(0.0, args.yaw_rate)
+    if test_name == "arc":
+        return IkCommand(args.speed, args.yaw_rate)
+    if follower is None:
+        raise RuntimeError(f"Test {test_name} needs a path follower")
+    del elapsed
+    return follower.update(sensor)
+
+
+def waypoints_for_test(test_name: str, args: argparse.Namespace) -> list[tuple[float, float]]:
+    sx = float(args.start_x)
+    sy = float(args.start_y)
+    if test_name == "waypoint":
+        return [
+            (sx + 0.3, sy),
+            (sx + 0.65, sy),
+            (sx + 1.0, sy),
+        ]
+    if test_name == "slalom":
+        local = [
+            (0.0, 0.0),
+            (0.8, -0.5),
+            (1.9, -0.95),
+            (2.8, -1.0),
+            (2.85, -1.8),
+            (2.75, -2.05),
+            (1.05, -2.0),
+            (0.9, -2.8),
+            (1.05, -3.0),
+            (2.75, -3.0),
+            (2.75, -3.72),
+            (2.25, -3.85),
+            (1.4, -4.25),
+            (1.4, -2.55),
+            (0.45, -3.42),
+            (0.0, 0.0),
+        ]
+        return [(sx + x, sy + y) for x, y in local]
+    return []
+
+
+@dataclass
+class Metrics:
+    start_pos: np.ndarray
+    start_yaw: float
+    start_time: float = 0.0
+    max_abs_roll: float = 0.0
+    max_abs_pitch: float = 0.0
+    max_tilt: float = 0.0
+    max_leg_error: float = 0.0
+    max_wheel_air_height: float = -1e9
+    min_wheel_air_height: float = 1e9
+    wheel_speed_error_sum: float = 0.0
+    wheel_speed_error_count: int = 0
+    max_wheel_speed: float = 0.0
+    max_gyro_z: float = 0.0
+    body_vx_sum: float = 0.0
+    gyro_z_sum: float = 0.0
+    yaw_unwrapped: float = 0.0
+    velocity_count: int = 0
+    last_yaw: float = field(init=False)
+
+    def __post_init__(self) -> None:
+        self.last_yaw = float(self.start_yaw)
+
+    def update(
+        self,
+        sensor: SensorFrame,
+        model: mujoco.MjModel,
+        data: mujoco.MjData,
+        ids: ModelIds,
+        geometry: WheelGeometry,
+        wheel_target: np.ndarray,
+        leg_target: np.ndarray,
+    ) -> None:
+        roll, pitch, _ = sensor.rpy
+        self.max_abs_roll = max(self.max_abs_roll, abs(float(roll)))
+        self.max_abs_pitch = max(self.max_abs_pitch, abs(float(pitch)))
+        self.max_tilt = max(self.max_tilt, math.hypot(float(roll), float(pitch)))
+        self.max_leg_error = max(
+            self.max_leg_error,
+            float(np.max(np.abs(sensor.joint_pos[:12] - leg_target))),
+        )
+
+        wheel_center_z = data.xipos[ids.wheel_body, 2]
+        floor_air_height = wheel_center_z - geometry.radius
+        self.max_wheel_air_height = max(self.max_wheel_air_height, float(np.max(floor_air_height)))
+        self.min_wheel_air_height = min(self.min_wheel_air_height, float(np.min(floor_air_height)))
+
+        actual = sensor.wheel_vel
+        self.wheel_speed_error_sum += float(np.mean(np.abs(actual - wheel_target)))
+        self.wheel_speed_error_count += 1
+        self.max_wheel_speed = max(self.max_wheel_speed, float(np.max(np.abs(actual))))
+        self.max_gyro_z = max(self.max_gyro_z, abs(float(sensor.gyro[2])))
+        rot = quat_to_mat_wxyz(sensor.quat_wxyz)
+        body_vel = rot.T @ data.qvel[:3]
+        yaw_now = float(sensor.rpy[2])
+        self.yaw_unwrapped += wrap_pi(yaw_now - self.last_yaw)
+        self.last_yaw = yaw_now
+        self.body_vx_sum += float(body_vel[0])
+        self.gyro_z_sum += float(sensor.gyro[2])
+        self.velocity_count += 1
+        del model
+
+    def as_dict(self, sensor: SensorFrame, reached: int, total: int, reason: str, success: bool) -> dict:
+        yaw_change = wrap_pi(float(sensor.rpy[2]) - self.start_yaw)
+        delta = sensor.base_pos[:2] - self.start_pos[:2]
+        mean_wheel_err = self.wheel_speed_error_sum / max(1, self.wheel_speed_error_count)
+        mean_body_vx = self.body_vx_sum / max(1, self.velocity_count)
+        elapsed = max(1e-6, float(sensor.time) - float(self.start_time))
+        mean_yaw_rate = self.yaw_unwrapped / elapsed
+        mean_gyro_z = self.gyro_z_sum / max(1, self.velocity_count)
+        return {
+            "success": bool(success),
+            "reason": reason,
+            "sim_time": round(sensor.time, 4),
+            "start_xy": [round(float(v), 4) for v in self.start_pos[:2]],
+            "final_xy": [round(float(v), 4) for v in sensor.base_pos[:2]],
+            "delta_xy": [round(float(v), 4) for v in delta],
+            "final_z": round(float(sensor.base_pos[2]), 4),
+            "yaw_change_deg": round(math.degrees(yaw_change), 3),
+            "final_yaw_deg": round(math.degrees(float(sensor.rpy[2])), 3),
+            "max_roll_deg": round(math.degrees(self.max_abs_roll), 3),
+            "max_pitch_deg": round(math.degrees(self.max_abs_pitch), 3),
+            "max_tilt_deg": round(math.degrees(self.max_tilt), 3),
+            "max_leg_encoder_error_rad": round(self.max_leg_error, 5),
+            "mean_wheel_speed_error_rad_s": round(mean_wheel_err, 5),
+            "max_wheel_speed_rad_s": round(self.max_wheel_speed, 5),
+            "mean_body_vx_mps": round(mean_body_vx, 5),
+            "mean_yaw_rate_rad_s": round(mean_yaw_rate, 5),
+            "mean_imu_gyro_z_rad_s": round(mean_gyro_z, 5),
+            "min_wheel_air_height_m": round(self.min_wheel_air_height, 5),
+            "max_wheel_air_height_m": round(self.max_wheel_air_height, 5),
+            "max_imu_gyro_z_rad_s": round(self.max_gyro_z, 5),
+            "reached_waypoints": int(reached),
+            "total_waypoints": int(total),
+        }
+
+
+class InteractivePanel:
+    def __init__(self, args: argparse.Namespace) -> None:
+        try:
+            import tkinter as tk
+            from tkinter import ttk
+        except ImportError as exc:
+            raise RuntimeError("Interactive mode needs tkinter, but it is not available in this Python.") from exc
+
+        self.tk = tk
+        self.alive = True
+        self.last_update = 0.0
+        self.root = tk.Tk()
+        self.root.title("Wheel-Leg IK Control")
+        self.root.protocol("WM_DELETE_WINDOW", self.close)
+
+        self.vx = tk.DoubleVar(value=float(args.speed))
+        self.yaw = tk.DoubleVar(value=float(args.yaw_rate))
+        self.status_vars: dict[str, object] = {}
+
+        main = ttk.Frame(self.root, padding=10)
+        main.grid(row=0, column=0, sticky="nsew")
+        self.root.columnconfigure(0, weight=1)
+        main.columnconfigure(1, weight=1)
+
+        self._add_slider(main, 0, "linear x m/s", self.vx, args.interactive_vx_limit)
+        self._add_slider(main, 1, "yaw z rad/s", self.yaw, args.interactive_yaw_limit)
+
+        buttons = ttk.Frame(main)
+        buttons.grid(row=2, column=0, columnspan=3, sticky="ew", pady=(6, 10))
+        ttk.Button(buttons, text="Zero", command=self.zero).pack(side="left", padx=(0, 6))
+        ttk.Button(buttons, text="Quit", command=self.close).pack(side="left")
+
+        metrics = ttk.LabelFrame(main, text="State", padding=8)
+        metrics.grid(row=3, column=0, columnspan=3, sticky="nsew")
+        metrics.columnconfigure(1, weight=1)
+        for row, key in enumerate(
+            [
+                "cmd",
+                "pos",
+                "rpy",
+                "body_vel",
+                "imu",
+                "wheel",
+                "wheel_air",
+                "leg",
+                "limits",
+            ]
+        ):
+            ttk.Label(metrics, text=key).grid(row=row, column=0, sticky="w", padx=(0, 8))
+            var = tk.StringVar(value="-")
+            self.status_vars[key] = var
+            ttk.Label(metrics, textvariable=var, width=48).grid(row=row, column=1, sticky="w")
+
+    def _add_slider(self, parent: object, row: int, label: str, var: object, limit: float) -> None:
+        ttk = __import__("tkinter.ttk").ttk
+        ttk.Label(parent, text=label).grid(row=row, column=0, sticky="w")
+        scale = ttk.Scale(parent, from_=-limit, to=limit, variable=var, orient="horizontal")
+        scale.grid(row=row, column=1, sticky="ew", padx=8)
+        value = ttk.Label(parent, width=8)
+        value.grid(row=row, column=2, sticky="e")
+
+        def refresh_value(*_: object) -> None:
+            value.configure(text=f"{float(var.get()): .2f}")
+
+        var.trace_add("write", refresh_value)
+        refresh_value()
+
+    def zero(self) -> None:
+        self.vx.set(0.0)
+        self.yaw.set(0.0)
+
+    def close(self) -> None:
+        self.alive = False
+
+    def command(self) -> IkCommand:
+        return IkCommand(float(self.vx.get()), float(self.yaw.get()))
+
+    def update_events(self) -> bool:
+        if not self.alive:
+            return False
+        try:
+            self.root.update_idletasks()
+            self.root.update()
+        except self.tk.TclError:
+            self.alive = False
+        return self.alive
+
+    def update_metrics(
+        self,
+        sensor: SensorFrame,
+        data: mujoco.MjData,
+        ids: ModelIds,
+        geometry: WheelGeometry,
+        wheel_target: np.ndarray,
+        leg_target: np.ndarray,
+        metrics: Metrics,
+        args: argparse.Namespace,
+    ) -> None:
+        if sensor.time - self.last_update < args.ui_update_dt:
+            return
+        self.last_update = sensor.time
+
+        rot = quat_to_mat_wxyz(sensor.quat_wxyz)
+        body_vel = rot.T @ data.qvel[:3]
+        wheel_air = data.xipos[ids.wheel_body, 2] - geometry.radius
+        wheel_err = float(np.mean(np.abs(sensor.wheel_vel - wheel_target)))
+        leg_err = float(np.max(np.abs(sensor.joint_pos[:12] - leg_target)))
+        tilt = math.hypot(float(sensor.rpy[0]), float(sensor.rpy[1]))
+
+        values = {
+            "cmd": f"x={self.vx.get(): .2f} m/s, yaw={self.yaw.get(): .2f} rad/s",
+            "pos": f"x={sensor.base_pos[0]: .3f}, y={sensor.base_pos[1]: .3f}, z={sensor.base_pos[2]: .3f}",
+            "rpy": (
+                f"roll={math.degrees(sensor.rpy[0]): .2f}, "
+                f"pitch={math.degrees(sensor.rpy[1]): .2f}, "
+                f"yaw={math.degrees(sensor.rpy[2]): .2f} deg"
+            ),
+            "body_vel": f"vx={body_vel[0]: .3f}, vy={body_vel[1]: .3f}, vz={body_vel[2]: .3f} m/s",
+            "imu": f"gyro_z={sensor.gyro[2]: .3f} rad/s, tilt={math.degrees(tilt): .2f} deg",
+            "wheel": (
+                f"target=[{', '.join(f'{v: .1f}' for v in wheel_target)}], "
+                f"err={wheel_err: .3f} rad/s"
+            ),
+            "wheel_air": f"min={np.min(wheel_air): .4f}, max={np.max(wheel_air): .4f} m",
+            "leg": f"encoder_err={leg_err: .4f} rad, max={metrics.max_leg_error: .4f} rad",
+            "limits": (
+                f"max_tilt={math.degrees(metrics.max_tilt): .2f} deg, "
+                f"max_wheel_speed={metrics.max_wheel_speed: .2f} rad/s"
+            ),
+        }
+        for key, value in values.items():
+            self.status_vars[key].set(value)
+
+    def destroy(self) -> None:
+        try:
+            self.root.destroy()
+        except self.tk.TclError:
+            pass
+
+
+def run_one(test_name: str, args: argparse.Namespace) -> dict:
+    tmp_root = Path(args.tmp_dir) if args.tmp_dir else Path(tempfile.gettempdir())
+    with tempfile.TemporaryDirectory(prefix="ik_slalom_", dir=str(tmp_root)) as tmp:
+        model, data, ids = build_model(args, Path(tmp))
+        default_dof_pos = reset_robot(model, data, ids, args)
+        geometry = infer_wheel_geometry(model, data, ids)
+        wheel_signs = np.array(args.wheel_signs, dtype=np.float64)
+        ik = DifferentialWheelIk(geometry, args.max_wheel_speed, wheel_signs, args.yaw_wheel_gain, args)
+        leg_ik = LegStanceIk(model, data, ids, args) if args.leg_ik else None
+        posture = TablePostureController(args, default_dof_pos) if leg_ik is None else None
+        path = waypoints_for_test(test_name, args)
+        if path and args.path_follower == "pure":
+            follower = PurePursuitFollower(path, args)
+        else:
+            follower = PathFollower(path, args.speed, args.max_yaw_rate, args.arrive_radius, args.allow_reverse) if path else None
+
+        sensor0 = read_sensors(model, data, ids)
+        metrics = Metrics(
+            start_pos=sensor0.base_pos.copy(),
+            start_yaw=float(sensor0.rpy[2]),
+            start_time=float(sensor0.time),
+        )
+
+        control_steps = max(1, int(round(args.control_dt / model.opt.timestep)))
+        total_steps = int(round(args.duration / model.opt.timestep))
+        wheel_target = np.zeros(4, dtype=np.float64)
+        leg_target = default_dof_pos[:12].copy()
+        reason = "timeout"
+        success = False
+
+        viewer = mujoco.viewer.launch_passive(model, data) if args.viewer else None
+        panel = InteractivePanel(args) if args.interactive else None
+        try:
+            for step in range(total_steps):
+                if panel is not None and not panel.update_events():
+                    reason = "interactive panel closed"
+                    break
+                if viewer is not None and not viewer.is_running():
+                    reason = "viewer closed"
+                    break
+
+                if step % control_steps == 0:
+                    sensor = read_sensors(model, data, ids)
+                    cmd = panel.command() if panel is not None else test_command(test_name, step * model.opt.timestep, sensor, follower, args)
+                    raw_wheel_target = ik.solve(cmd, sensor)
+                    if leg_ik is not None:
+                        leg_target = leg_ik.compute(data, sensor)
+                    elif posture is not None:
+                        leg_target = posture.compute(sensor)
+                    raw_wheel_target *= sensor_command_scale(sensor, leg_target, args)
+                    max_wheel_delta = args.wheel_accel_limit * args.control_dt
+                    wheel_target = wheel_target + np.clip(raw_wheel_target - wheel_target, -max_wheel_delta, max_wheel_delta)
+                    data.ctrl[ids.ctrl[:12]] = leg_target
+                    data.ctrl[ids.ctrl[12:]] = wheel_target
+
+                    if follower is not None and follower.done:
+                        reason = "path complete"
+                        success = True
+                        break
+
+                mujoco.mj_step(model, data)
+                if step % control_steps == 0:
+                    sensor = read_sensors(model, data, ids)
+                    metrics.update(sensor, model, data, ids, geometry, wheel_target, leg_target)
+                    if panel is not None:
+                        panel.update_metrics(sensor, data, ids, geometry, wheel_target, leg_target, metrics, args)
+                    if viewer is not None:
+                        viewer.sync()
+
+                if viewer is not None and args.realtime:
+                    time.sleep(model.opt.timestep * args.realtime_scale)
+        finally:
+            if panel is not None:
+                panel.destroy()
+            if viewer is not None:
+                viewer.close()
+
+        final_sensor = read_sensors(model, data, ids)
+        reached = follower.idx if follower is not None else 0
+        total = len(path)
+
+        stable = (
+            metrics.max_tilt < math.radians(args.max_tilt_deg)
+            and metrics.max_leg_error < args.max_leg_error
+            and metrics.max_wheel_air_height < args.max_wheel_air_height
+        )
+
+        if args.interactive:
+            success = stable
+            reason = reason if reason != "timeout" else "interactive duration elapsed"
+        elif test_name == "forward":
+            success = final_sensor.base_pos[0] - metrics.start_pos[0] > max(0.25, 0.35 * args.speed * args.duration) and stable
+            reason = "forward displacement ok" if success else reason
+        elif test_name == "yaw":
+            success = abs(wrap_pi(final_sensor.rpy[2] - metrics.start_yaw)) > max(0.15, 0.14 * abs(args.yaw_rate) * args.duration) and stable
+            reason = "yaw change ok" if success else reason
+        elif test_name == "arc":
+            delta = np.linalg.norm(final_sensor.base_pos[:2] - metrics.start_pos[:2])
+            success = (
+                delta > max(0.25, 0.25 * args.speed * args.duration)
+                and abs(wrap_pi(final_sensor.rpy[2] - metrics.start_yaw)) > 0.15
+                and stable
+            )
+            reason = "arc displacement and yaw ok" if success else reason
+        elif test_name in {"waypoint", "slalom"}:
+            success = follower is not None and follower.done and stable
+            reason = "path complete" if success else reason
+
+        out = metrics.as_dict(final_sensor, reached, total, reason, success)
+        out.update(
+            {
+                "test": test_name,
+                "wheel_radius_m": round(geometry.radius, 5),
+                "track_width_m": round(geometry.track_width, 5),
+                "wheel_body_y_m": [round(float(v), 5) for v in geometry.wheel_y],
+                "wheel_signs": [float(v) for v in wheel_signs],
+                "wheel_model": args.wheel_model,
+                "linear_wheel_gain": float(args.linear_wheel_gain),
+                "direct_yaw_wheel_gain": float(args.direct_yaw_wheel_gain),
+                "yaw_wheel_gain": float(args.yaw_wheel_gain),
+                "stable": bool(stable),
+                "leg_ik": bool(args.leg_ik),
+                "posture": args.posture,
+                "body_height": float(args.body_height),
+                "imu_posture": bool(args.imu_posture),
+                "yaw_rate_kp": float(args.yaw_rate_kp),
+                "encoder_posture_kp": float(args.encoder_posture_kp),
+                "encoder_guard": bool(args.encoder_guard),
+                "imu_guard": bool(args.imu_guard),
+                "posture_gain": float(args.posture_gain),
+                "path_follower": args.path_follower,
+                "default_pose": (
+                    "rough_rl"
+                    if args.posture == "rough"
+                    else "custom"
+                    if args.posture == "custom"
+                    else "mujoco_sim_height_table"
+                ),
+            }
+        )
+        return out
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--test", choices=("forward", "yaw", "arc", "waypoint", "slalom", "all"), default="forward")
+    parser.add_argument("--duration", type=float, default=8.0)
+    parser.add_argument("--settle", type=float, default=1.5)
+    parser.add_argument("--control-dt", type=float, default=0.02)
+    parser.add_argument("--speed", type=float, default=0.35)
+    parser.add_argument("--yaw-rate", type=float, default=0.25)
+    parser.add_argument("--max-yaw-rate", type=float, default=0.45)
+    parser.add_argument("--max-wheel-speed", type=float, default=10.0)
+    parser.add_argument("--wheel-model", choices=("diff", "direct"), default="diff")
+    parser.add_argument("--linear-wheel-gain", type=float, default=12.5)
+    parser.add_argument("--direct-yaw-wheel-gain", type=float, default=8.0)
+    parser.add_argument("--yaw-wheel-gain", type=float, default=2.2)
+    parser.add_argument("--yaw-rate-kp", type=float, default=0.0)
+    parser.add_argument("--max-yaw-wheel-speed", type=float, default=1.65)
+    parser.add_argument("--wheel-accel-limit", type=float, default=28.0)
+    parser.add_argument("--max-tilt-deg", type=float, default=25.0)
+    parser.add_argument("--max-leg-error", type=float, default=0.65)
+    parser.add_argument("--max-wheel-air-height", type=float, default=0.08)
+    parser.add_argument("--posture", choices=("rough", "table", "custom"), default="rough")
+    parser.add_argument("--body-height", type=float, default=0.37)
+    parser.add_argument("--custom-abduction", type=float, default=0.0)
+    parser.add_argument("--custom-hip", type=float, default=0.55)
+    parser.add_argument("--custom-knee", type=float, default=-1.30)
+    parser.add_argument("--imu-posture", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--roll-comp-gain", type=float, default=0.35)
+    parser.add_argument("--pitch-comp-gain", type=float, default=0.35)
+    parser.add_argument("--encoder-posture-kp", type=float, default=0.15)
+    parser.add_argument("--encoder-posture-max", type=float, default=0.03)
+    parser.add_argument("--encoder-guard", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--encoder-guard-start", type=float, default=0.28)
+    parser.add_argument("--encoder-guard-stop", type=float, default=0.65)
+    parser.add_argument("--imu-guard", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--imu-guard-start-deg", type=float, default=12.0)
+    parser.add_argument("--imu-guard-stop-deg", type=float, default=28.0)
+    parser.add_argument("--leg-ik", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument("--posture-gain", type=float, default=0.65)
+    parser.add_argument("--leg-ik-iterations", type=int, default=3)
+    parser.add_argument("--leg-ik-damping", type=float, default=0.02)
+    parser.add_argument("--leg-ik-step", type=float, default=0.035)
+    parser.add_argument("--max-leg-offset", type=float, default=0.28)
+    parser.add_argument("--leg-target-rate", type=float, default=0.035)
+    parser.add_argument("--leg-target-filter", type=float, default=0.45)
+    parser.add_argument("--arrive-radius", type=float, default=0.20)
+    parser.add_argument("--allow-reverse", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--path-follower", choices=("waypoint", "pure"), default="pure")
+    parser.add_argument("--lookahead-base", type=float, default=0.45)
+    parser.add_argument("--lookahead-time", type=float, default=0.35)
+    parser.add_argument("--lookahead-min", type=float, default=0.35)
+    parser.add_argument("--lookahead-max", type=float, default=0.90)
+    parser.add_argument("--max-lat-acc", type=float, default=0.45)
+    parser.add_argument("--min-path-speed", type=float, default=0.25)
+    parser.add_argument("--pure-search-segments", type=int, default=4)
+    parser.add_argument("--start-x", type=float, default=0.0)
+    parser.add_argument("--start-y", type=float, default=0.0)
+    parser.add_argument("--start-z", type=float, default=0.6)
+    parser.add_argument(
+        "--terrain-xml",
+        type=Path,
+        default=None,
+        help="Optional terrain scene XML. Omit it to use a generated flat ground scene.",
+    )
+    parser.add_argument("--robot-xml", type=Path, default=PROJECT_ROOT / "mjcf" / "wheelleg.xml")
+    parser.add_argument("--tmp-dir", type=Path, default=Path("D:/tmp") if Path("D:/tmp").exists() else None)
+    parser.add_argument("--interactive", action="store_true", help="Open a live command/metrics panel and drive manually.")
+    parser.add_argument("--interactive-vx-limit", type=float, default=1.0)
+    parser.add_argument("--interactive-yaw-limit", type=float, default=1.0)
+    parser.add_argument("--ui-update-dt", type=float, default=0.10)
+    parser.add_argument("--viewer", action="store_true", help="Open MuJoCo passive viewer while running the test.")
+    parser.add_argument("--realtime", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--realtime-scale", type=float, default=1.0)
+    parser.add_argument(
+        "--wheel-signs",
+        type=float,
+        nargs=4,
+        default=[1.0, 1.0, 1.0, 1.0],
+        metavar=("FL", "FR", "RL", "RR"),
+        help="Per-wheel velocity sign multipliers in joint order.",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    if args.interactive:
+        args.viewer = True
+        args.duration = max(args.duration, 3600.0)
+    tests = ["forward", "yaw", "arc", "waypoint", "slalom"] if args.test == "all" else [args.test]
+    results = [run_one(name, args) for name in tests]
+    print(json.dumps(results[0] if len(results) == 1 else results, indent=2, ensure_ascii=False))
+    return 0 if all(r["success"] for r in results) else 2
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/05_software/train/rc_mjlab/sim2sim/interface/mujoco_io.py b/05_software/train/rc_mjlab/sim2sim/interface/mujoco_io.py
index 276aa64..a7447f7 100644
--- a/05_software/train/rc_mjlab/sim2sim/interface/mujoco_io.py
+++ b/05_software/train/rc_mjlab/sim2sim/interface/mujoco_io.py
@@ -87,9 +87,14 @@ class MuJoCoIO:
         return out_xml_path
 
     def _rebuild_actuators(self, spec):
-        actuators_to_delete = list(spec.actuators)
-        for act in actuators_to_delete:
-            spec.delete(act)
+        if hasattr(spec, "delete"):
+            actuators_to_delete = list(spec.actuators)
+            for act in actuators_to_delete:
+                spec.delete(act)
+        else:
+            actuators_to_delete = list(spec.actuators)
+            for act in actuators_to_delete:
+                act.delete()
         
         # Keep sim2sim aligned with the training robot config and sim2real runtime:
         # leg position PD = (50.0, 1.5), wheel velocity damping = 1.0.
diff --git a/05_software/train/rc_mjlab/sim2sim/nav_route_sim2sim_check.py b/05_software/train/rc_mjlab/sim2sim/nav_route_sim2sim_check.py
new file mode 100644
index 0000000..4425482
--- /dev/null
+++ b/05_software/train/rc_mjlab/sim2sim/nav_route_sim2sim_check.py
@@ -0,0 +1,2776 @@
+#!/usr/bin/env python3
+"""Batch sim2sim route validation for nav_tools waypoints.
+
+This script runs the MuJoCo robot with an ONNX locomotion policy and a simple
+waypoint follower, then reports whether the route can be completed without
+falling or entering avoid-region clearance.
+
+Example:
+    uv run python sim2sim/nav_route_sim2sim_check.py \
+        --terrain-xml tools/nav_tools/xml/A.xml \
+        --points tools/nav_tools/points/points_20260705_174627.json \
+        --onnx model_6800.onnx \
+        --start-yaw-offset-deg -180 \
+        --heading-offset-deg 180
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import os
+import pkgutil
+import random
+import sys
+import time
+import xml.etree.ElementTree as ET
+from collections import deque
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import mujoco
+import mujoco.viewer
+import numpy as np
+import onnxruntime as ort
+import torch
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+SIM2SIM_DIR = Path(__file__).resolve().parent
+if str(SIM2SIM_DIR) not in sys.path:
+    sys.path.insert(0, str(SIM2SIM_DIR))
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from interface.mujoco_io import MuJoCoIO  # noqa: E402
+from policy.policy_runner import PolicyRunner  # noqa: E402
+from tools.nav_tools.route_safety_check import (  # noqa: E402
+    AvoidRegion,
+    Waypoint,
+    default_lateral_footprint_radius,
+    load_regions,
+    load_waypoints,
+    point_in_polygon,
+    point_segment_distance,
+)
+
+
+ACTION_SCALE = np.array(
+    [0.125, 0.25, 0.25] * 4 + [5.0, 5.0, 5.0, 5.0],
+    dtype=np.float32,
+)
+DEPLOY_DEFAULT_DOF_POS = np.array([0.0, 0.550, -1.125] * 4 + [0.0] * 4, dtype=np.float32)
+ROBOT_BODY_LENGTH = 0.356
+ROBOT_BODY_WIDTH = 0.235
+ROBOT_BODY_CENTER_X = 0.1518
+ROBOT_ORIGIN_FROM_FRONT = 0.105
+ROBOT_BODY_CENTER_OFFSET_X = ROBOT_ORIGIN_FROM_FRONT - ROBOT_BODY_LENGTH * 0.5
+ROBOT_WHEEL_VIS_LENGTH = 0.16
+ROBOT_WHEEL_VIS_WIDTH = 0.055
+ROBOT_WHEEL_POSITIONS = {
+    "fl": ((0.32826 + 0.06389) - ROBOT_BODY_CENTER_X, 0.066172 - 0.027344, 0.1035, 0.014699, 0.04074, 0.0),
+    "fr": ((0.32826 + 0.06389) - ROBOT_BODY_CENTER_X, -0.065853 + 0.027311, -0.1035, -0.018447, -0.040735, -0.00075079),
+    "rl": ((-0.024743 - 0.06389) - ROBOT_BODY_CENTER_X, 0.066141 - 0.027309, 0.099459, 0.012475, 0.040737, 0.0),
+    "rr": ((-0.024743 - 0.06389) - ROBOT_BODY_CENTER_X, -0.065884 + 0.027341, -0.099408, -0.012435, -0.040737, -0.00075079),
+}
+
+
+@dataclass
+class SimConfig:
+    control_hz: float = 50.0
+    max_vx: float = 1.2
+    max_vy: float = 0.0
+    max_wz: float = 0.8
+    kp_dist: float = 0.8
+    kp_yaw: float = 1.8
+    yaw_stop_threshold_deg: float = 45.0
+    turn_in_place_enter_deg: float = 70.0
+    turn_in_place_exit_deg: float = 18.0
+    turn_in_place_max_wz: float = 0.8
+    final_align_max_wz: float = 0.45
+    final_align_kp_scale: float = 0.6
+    waypoint_timeout_s: float = 30.0
+    max_total_time_s: float = 420.0
+    stable_cycles: int = 2
+    lookahead_m: float = 0.45
+    min_cmd_vx: float = 0.08
+    creep_cmd_vx: float = 0.04
+    cmd_vx_scale: float = 1.0
+    stuck_timeout_s: float = 4.0
+    stuck_progress_m: float = 0.08
+    recovery_duration_s: float = 2.0
+    max_recoveries: int = 4
+    slalom_script_enabled: bool = True
+    slalom_script_start_tolerance: float = 0.10
+    slalom_script_pos_tolerance: float = 0.08
+    slalom_script_yaw_tolerance_deg: float = 5.0
+    slalom_script_stable_cycles: int = 1
+    slalom_script_rotate_steps_enabled: bool = False
+    slalom_script_final_rotate_enabled: bool = False
+    slalom_script_require_yaw_at_step: bool = False
+    slalom_script_kp_dist: float = 1.0
+    slalom_script_kp_yaw: float = 1.6
+    slalom_script_max_vx: float = 0.60
+    slalom_script_max_vy: float = 0.50
+    slalom_script_max_wz: float = 0.50
+    slalom_script_min_cmd_linear: float = 0.2
+    slalom_script_min_cmd_angular: float = 0.0
+    slalom_script_min_cmd_epsilon: float = 0.05
+    slalom_script_min_step_distance: float = 0.02
+    slalom_script_yaw_gate_deg: float = 25.0
+    slalom_script_drive_yaw_source: str = "segment"
+    slalom_script_curvature_speed_enabled: bool = True
+    slalom_script_curvature_min_scale: float = 0.55
+
+
+@dataclass(frozen=True)
+class SlalomScriptStep:
+    kind: str
+    start_index: int
+    end_index: int
+    target_x: float
+    target_y: float
+    target_yaw: float | None
+    pos_tolerance: float | None = None
+    forward: float = 0.0
+    left: float = 0.0
+
+
+@dataclass
+class SimResult:
+    success: bool
+    reason: str
+    sim_time: float
+    reached_count: int
+    waypoint_count: int
+    min_clearance: float
+    min_margin: float
+    min_clearance_region: str
+    min_clearance_wp: str
+    max_roll_deg: float
+    max_pitch_deg: float
+    max_tilt_deg: float
+    samples: int
+    wall_time: float
+    real_time_factor: float
+
+
+@dataclass
+class RouteSnapshot:
+    time: float
+    qpos: np.ndarray
+    qvel: np.ndarray
+    ctrl: np.ndarray
+    follower_index: int
+    follower_turn_in_place: bool
+    follower_stable_count: int
+    follower_wp_start_time: float
+    follower_best_dist: float | None
+    follower_last_progress_time: float | None
+    follower_recovery_until: float | None
+    follower_recovery_count: int | None
+    follower_recovery_turn_sign: float | None
+
+
+class OnnxPolicy:
+    def __init__(self, path: Path) -> None:
+        providers = ["CPUExecutionProvider"]
+        self.session = ort.InferenceSession(str(path), providers=providers)
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_name = self.session.get_outputs()[0].name
+        shape = self.session.get_inputs()[0].shape
+        dim = shape[1] if len(shape) >= 2 else 53
+        self.obs_dim = 53 if isinstance(dim, str) else int(dim)
+        self.last_actions = np.zeros(16, dtype=np.float32)
+
+    def reset(self) -> None:
+        self.last_actions[:] = 0.0
+
+    def step(self, obs_53d: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        if self.obs_dim == obs_53d.shape[0]:
+            model_input = obs_53d
+        else:
+            raise RuntimeError(
+                f"ONNX expects obs dim {self.obs_dim}, but this checker provides {obs_53d.shape[0]}"
+            )
+        raw = self.session.run(
+            [self.output_name],
+            {self.input_name: model_input.astype(np.float32)[None, :]},
+        )[0][0].astype(np.float32)
+        raw = np.clip(raw, -100.0, 100.0)
+        self.last_actions = raw.copy()
+        return raw * ACTION_SCALE, raw
+
+
+class ExistingPolicyBackend:
+    """Adapter around sim2sim/policy/PolicyRunner to match nav_sim2sim.py."""
+
+    def __init__(self, path: Path, crawl_path: Path | None = None) -> None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        policy_path: Path | dict[str, Path]
+        if crawl_path is not None:
+            policy_path = {"rough": path, "crawl": crawl_path}
+        else:
+            policy_path = path
+        self.runner = PolicyRunner(policy_path, device)
+        self.runner.reset()
+
+    @property
+    def default_dof_pos(self) -> np.ndarray:
+        return self.runner.default_dof_pos
+
+    @property
+    def last_actions(self) -> np.ndarray:
+        return self.runner.last_actions
+
+    def reset(self) -> None:
+        self.runner.reset()
+
+    def maybe_switch_policy(self, policy_name: str | None) -> None:
+        if not policy_name:
+            return
+        if policy_name == self.runner.current_policy_name:
+            return
+        if self.runner.transition_in_progress and policy_name == self.runner.current_policy_name:
+            return
+        if policy_name in self.runner.policies:
+            self.runner.trigger_transition(policy_name)
+
+    def step(self, obs_53d: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        return self.runner.step(obs_53d)
+
+    def close(self) -> None:
+        listener = getattr(self.runner, "listener", None)
+        if listener is not None:
+            listener.stop()
+
+
+class RoughPolicyIkCrawlBackend(ExistingPolicyBackend):
+    def __init__(self, path: Path) -> None:
+        super().__init__(path, None)
+        self.mode = "rough"
+        self.requested_mode = "rough"
+        self.crawl_default_dof_pos = np.array(
+            [
+                0.2, 1.697, -2.650,
+                -0.2, 1.697, -2.650,
+                0.2, 1.697, -2.650,
+                -0.2, 1.697, -2.650,
+                0.0, 0.0, 0.0, 0.0,
+            ],
+            dtype=np.float32,
+        )
+        self.slalom_ik_default_dof_pos = np.array(
+            [
+                0.0, 1.697, -2.650,
+                0.0, 1.697, -2.650,
+                0.0, 1.697, -2.650,
+                0.0, 1.697, -2.650,
+                0.0, 0.0, 0.0, 0.0,
+            ],
+            dtype=np.float32,
+        )
+        self._last_actions = np.zeros(16, dtype=np.float32)
+        self.crawl_ik_wheel_linear_gain = 6.25
+        self.crawl_ik_wheel_yaw_gain = 4.0
+        self.crawl_ik_max_wheel_speed = 6.0
+        self.transition_steps = 60
+        self.transition_step = self.transition_steps
+        self.transition_start_pose = self.runner.default_dof_pos.copy()
+        self.transition_target_pose = self.runner.default_dof_pos.copy()
+
+    def reset(self) -> None:
+        self.runner.reset()
+        self.mode = "rough"
+        self.requested_mode = "rough"
+        self.transition_step = self.transition_steps
+        self.transition_start_pose = self.runner.default_dof_pos.copy()
+        self.transition_target_pose = self.runner.default_dof_pos.copy()
+        self._last_actions[:] = 0.0
+
+    @property
+    def default_dof_pos(self) -> np.ndarray:
+        if self.transition_step < self.transition_steps:
+            alpha = self.transition_step / max(1, self.transition_steps)
+            return (1.0 - alpha) * self.transition_start_pose + alpha * self.transition_target_pose
+        if self.requested_mode == "crawl":
+            return self.crawl_default_dof_pos
+        if self.requested_mode == "slalom_ik":
+            return self.slalom_ik_default_dof_pos
+        return self.runner.default_dof_pos
+
+    @property
+    def last_actions(self) -> np.ndarray:
+        if self.requested_mode in {"crawl", "slalom_ik"}:
+            return self._last_actions
+        return self.runner.last_actions
+
+    def maybe_switch_policy(self, policy_name: str | None) -> None:
+        normalized = (policy_name or "").lower()
+        if normalized in {"slalom", "slalom_ik", "slalomik"}:
+            next_mode = "slalom_ik"
+        elif normalized in {"crawl", "ik"}:
+            next_mode = "crawl"
+        else:
+            next_mode = "rough"
+        if next_mode == self.requested_mode:
+            return
+        self.transition_start_pose = self.default_dof_pos.copy()
+        if next_mode == "crawl":
+            self.transition_target_pose = self.crawl_default_dof_pos.copy()
+        elif next_mode == "slalom_ik":
+            self.transition_target_pose = self.slalom_ik_default_dof_pos.copy()
+        else:
+            self.transition_target_pose = self.runner.default_dof_pos.copy()
+        self.transition_step = 0
+        self.requested_mode = next_mode
+
+    def step(self, obs_53d: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        in_transition = self.transition_step < self.transition_steps
+        if in_transition:
+            self.transition_step += 1
+        if self.requested_mode not in {"crawl", "slalom_ik"}:
+            return self.runner.step(obs_53d)
+        cmd = obs_53d[6:9].astype(np.float32)
+        target = (
+            self.slalom_ik_default_dof_pos.copy()
+            if self.requested_mode == "slalom_ik"
+            else self.crawl_default_dof_pos.copy()
+        )
+        left_wheel = float(np.clip(
+            cmd[0] * self.crawl_ik_wheel_linear_gain - cmd[2] * self.crawl_ik_wheel_yaw_gain,
+            -self.crawl_ik_max_wheel_speed,
+            self.crawl_ik_max_wheel_speed,
+        ))
+        right_wheel = float(np.clip(
+            cmd[0] * self.crawl_ik_wheel_linear_gain + cmd[2] * self.crawl_ik_wheel_yaw_gain,
+            -self.crawl_ik_max_wheel_speed,
+            self.crawl_ik_max_wheel_speed,
+        ))
+        alpha = 1.0
+        if in_transition:
+            alpha = min(1.0, self.transition_step / max(1, self.transition_steps))
+        scaled = target.copy()
+        scaled[12:] = [left_wheel, right_wheel, left_wheel, right_wheel]
+        scaled[12:] *= alpha
+        raw = np.zeros(16, dtype=np.float32)
+        raw[12:] = (np.array([left_wheel, right_wheel, left_wheel, right_wheel], dtype=np.float32) * alpha) / 5.0
+        self._last_actions = raw
+        # send_actions adds default_dof_pos to scaled actions, so return leg offsets.
+        scaled[:12] = 0.0
+        return scaled, raw
+
+
+class DirectOnnxBackend:
+    """Lightweight ONNX adapter kept for isolating PolicyRunner effects."""
+
+    def __init__(self, path: Path, default_dof_pos: np.ndarray) -> None:
+        self.policy = OnnxPolicy(path)
+        self._default_dof_pos = default_dof_pos.astype(np.float32)
+
+    @property
+    def default_dof_pos(self) -> np.ndarray:
+        return self._default_dof_pos
+
+    @property
+    def last_actions(self) -> np.ndarray:
+        return self.policy.last_actions
+
+    def reset(self) -> None:
+        self.policy.reset()
+
+    def maybe_switch_policy(self, policy_name: str | None) -> None:
+        _ = policy_name
+
+    def step(self, obs_53d: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        return self.policy.step(obs_53d)
+
+    def close(self) -> None:
+        pass
+
+
+class WaypointFollower:
+    def __init__(self, waypoints: list[Waypoint], cfg: SimConfig) -> None:
+        self.waypoints = waypoints
+        self.cfg = cfg
+        self.index = 0
+        self.turn_in_place = False
+        self.stable_count = 0
+        self.wp_start_time = 0.0
+
+    @property
+    def active(self) -> Waypoint | None:
+        if self.index >= len(self.waypoints):
+            return None
+        return self.waypoints[self.index]
+
+    def reset_timing(self, sim_time: float) -> None:
+        self.wp_start_time = sim_time
+
+    @staticmethod
+    def normalize_angle(value: float) -> float:
+        while value > math.pi:
+            value -= 2.0 * math.pi
+        while value < -math.pi:
+            value += 2.0 * math.pi
+        return value
+
+    @staticmethod
+    def clamp(value: float, lo: float, hi: float) -> float:
+        return max(lo, min(hi, value))
+
+    def update(self, x: float, y: float, yaw: float, sim_time: float) -> tuple[np.ndarray, str | None]:
+        wp = self.active
+        if wp is None:
+            return np.zeros(3, dtype=np.float32), "complete"
+
+        if sim_time - self.wp_start_time > self.cfg.waypoint_timeout_s:
+            return np.zeros(3, dtype=np.float32), f"timeout at waypoint {wp.id}"
+
+        dx = wp.x - x
+        dy = wp.y - y
+        dist = math.hypot(dx, dy)
+        tolerance = wp.tolerance if wp.tolerance is not None else 0.15
+        if dist <= tolerance:
+            self.stable_count += 1
+            if self.stable_count >= self.cfg.stable_cycles:
+                self.index += 1
+                self.turn_in_place = False
+                self.stable_count = 0
+                self.wp_start_time = sim_time
+                if self.index >= len(self.waypoints):
+                    return np.zeros(3, dtype=np.float32), "complete"
+                return np.zeros(3, dtype=np.float32), None
+            return np.zeros(3, dtype=np.float32), None
+        self.stable_count = 0
+
+        target_yaw = math.atan2(dy, dx)
+        yaw_err = self.normalize_angle(target_yaw - yaw)
+        yaw_stop_threshold = math.radians(self.cfg.yaw_stop_threshold_deg)
+
+        if self.turn_in_place:
+            if abs(yaw_err) <= math.radians(self.cfg.turn_in_place_exit_deg):
+                self.turn_in_place = False
+        elif abs(yaw_err) >= math.radians(self.cfg.turn_in_place_enter_deg):
+            self.turn_in_place = True
+
+        if self.turn_in_place:
+            return np.array(
+                [
+                    0.0,
+                    0.0,
+                    self.clamp(
+                        self.cfg.kp_yaw * yaw_err,
+                        -self.cfg.turn_in_place_max_wz,
+                        self.cfg.turn_in_place_max_wz,
+                    ),
+                ],
+                dtype=np.float32,
+            ), None
+
+        speed_limit = self.cfg.max_vx if wp.speed is None else min(self.cfg.max_vx, max(0.0, wp.speed))
+        cmd = np.zeros(3, dtype=np.float32)
+        cmd[2] = self.clamp(self.cfg.kp_yaw * yaw_err, -self.cfg.max_wz, self.cfg.max_wz)
+        if abs(yaw_err) <= yaw_stop_threshold:
+            cmd[0] = self.clamp(self.cfg.kp_dist * dist * math.cos(yaw_err), 0.0, speed_limit)
+        cmd[0] *= self.cfg.cmd_vx_scale
+        return cmd, None
+
+
+class PurePursuitFollower(WaypointFollower):
+    def __init__(self, waypoints: list[Waypoint], cfg: SimConfig) -> None:
+        super().__init__(waypoints, cfg)
+        self.best_dist = float("inf")
+        self.last_progress_time = 0.0
+        self.recovery_until = -1.0
+        self.recovery_count = 0
+        self.recovery_turn_sign = 1.0
+
+    def reset_timing(self, sim_time: float) -> None:
+        super().reset_timing(sim_time)
+        self.best_dist = float("inf")
+        self.last_progress_time = sim_time
+        self.recovery_until = -1.0
+        self.recovery_count = 0
+
+    def _advance_reached(self, x: float, y: float, sim_time: float) -> str | None:
+        while self.active is not None:
+            wp = self.active
+            if wp.require_yaw:
+                break
+            tol = wp.tolerance if wp.tolerance is not None else 0.15
+            if wp.mandatory_cross:
+                tol = min(tol, 0.15 if wp.mandatory_radius is None else max(0.01, wp.mandatory_radius))
+            completion_x = wp.x if wp.mandatory_center_x is None else wp.mandatory_center_x
+            completion_y = wp.y if wp.mandatory_center_y is None else wp.mandatory_center_y
+            if math.hypot(completion_x - x, completion_y - y) > tol:
+                break
+            self.index += 1
+            self.turn_in_place = False
+            self.stable_count = 0
+            self.wp_start_time = sim_time
+            self.best_dist = float("inf")
+            self.last_progress_time = sim_time
+        if self.index >= len(self.waypoints):
+            return "complete"
+        return None
+
+    def _lookahead_target(self, x: float, y: float) -> Waypoint:
+        active = self.active
+        if active is None:
+            return self.waypoints[-1]
+        if active.exact_reach:
+            return active
+        target = active
+        for wp in self.waypoints[self.index :]:
+            target = wp
+            if wp.mandatory_cross:
+                mandatory_radius = 0.15 if wp.mandatory_radius is None else max(0.01, wp.mandatory_radius)
+                center_x = wp.x if wp.mandatory_center_x is None else wp.mandatory_center_x
+                center_y = wp.y if wp.mandatory_center_y is None else wp.mandatory_center_y
+                if math.hypot(center_x - x, center_y - y) > mandatory_radius:
+                    break
+            if math.hypot(wp.x - x, wp.y - y) >= self.cfg.lookahead_m:
+                break
+        return target
+
+    def _current_speed_limit(self) -> float:
+        speed = self.cfg.max_vx
+        end = min(len(self.waypoints), self.index + 4)
+        for wp in self.waypoints[self.index : end]:
+            if wp.speed is not None:
+                speed = min(speed, max(0.0, wp.speed))
+        return speed
+
+    def update(self, x: float, y: float, yaw: float, sim_time: float) -> tuple[np.ndarray, str | None]:
+        nav_state = self._advance_reached(x, y, sim_time)
+        if nav_state is not None:
+            return np.zeros(3, dtype=np.float32), nav_state
+
+        active = self.active
+        if active is None:
+            return np.zeros(3, dtype=np.float32), "complete"
+        active_dist = math.hypot(active.x - x, active.y - y)
+        if active.require_yaw and active_dist <= (active.tolerance if active.tolerance is not None else 0.15):
+            yaw_tolerance_deg = 5.0 if active.yaw_tolerance_deg is None else max(0.0, active.yaw_tolerance_deg)
+            yaw_err = self.normalize_angle(math.radians(active.yaw_deg) - yaw)
+            if abs(yaw_err) > math.radians(yaw_tolerance_deg):
+                self.stable_count = 0
+                return np.array(
+                    [
+                        0.0,
+                        0.0,
+                        self.clamp(
+                            self.cfg.kp_yaw * self.cfg.final_align_kp_scale * yaw_err,
+                            -self.cfg.final_align_max_wz,
+                            self.cfg.final_align_max_wz,
+                        ),
+                    ],
+                    dtype=np.float32,
+                ), None
+            required_stable_cycles = (
+                self.cfg.stable_cycles if active.stable_cycles is None else max(1, active.stable_cycles)
+            )
+            self.stable_count += 1
+            if self.stable_count < required_stable_cycles:
+                return np.zeros(3, dtype=np.float32), None
+            self.index += 1
+            self.turn_in_place = False
+            self.stable_count = 0
+            self.wp_start_time = sim_time
+            self.best_dist = float("inf")
+            self.last_progress_time = sim_time
+            if self.index >= len(self.waypoints):
+                return np.zeros(3, dtype=np.float32), "complete"
+            return np.zeros(3, dtype=np.float32), None
+        if active_dist + self.cfg.stuck_progress_m < self.best_dist:
+            self.best_dist = active_dist
+            self.last_progress_time = sim_time
+
+        if self.recovery_until > sim_time:
+            return np.array(
+                [
+                    -0.10 * abs(self.cfg.cmd_vx_scale),
+                    0.0,
+                    0.45 * self.recovery_turn_sign,
+                ],
+                dtype=np.float32,
+            ), None
+
+        if sim_time - self.wp_start_time > self.cfg.waypoint_timeout_s:
+            return np.zeros(3, dtype=np.float32), f"timeout at waypoint {active.id}"
+
+        if sim_time - self.last_progress_time > self.cfg.stuck_timeout_s:
+            if active.require_yaw:
+                self.last_progress_time = sim_time
+            elif self.recovery_count >= self.cfg.max_recoveries:
+                return np.zeros(3, dtype=np.float32), f"stuck near waypoint {active.id}"
+            else:
+                self.recovery_count += 1
+                self.recovery_turn_sign *= -1.0
+                self.recovery_until = sim_time + self.cfg.recovery_duration_s
+                self.last_progress_time = sim_time
+                return np.array([-0.10 * abs(self.cfg.cmd_vx_scale), 0.0, 0.45 * self.recovery_turn_sign], dtype=np.float32), None
+
+        target = self._lookahead_target(x, y)
+        dx = target.x - x
+        dy = target.y - y
+        target_dist = math.hypot(dx, dy)
+        target_yaw = math.atan2(dy, dx)
+        yaw_err = self.normalize_angle(target_yaw - yaw)
+
+        cmd = np.zeros(3, dtype=np.float32)
+        cmd[2] = self.clamp(self.cfg.kp_yaw * yaw_err, -self.cfg.max_wz, self.cfg.max_wz)
+        if abs(yaw_err) > math.radians(self.cfg.turn_in_place_enter_deg):
+            cmd[0] = 0.0
+            return cmd, None
+
+        speed_limit = self._current_speed_limit()
+        curvature_slow = max(0.35, 1.0 - abs(yaw_err) / math.radians(80.0))
+        cmd_vx = self.cfg.kp_dist * target_dist * math.cos(yaw_err)
+        cmd_vx = self.clamp(cmd_vx, self.cfg.min_cmd_vx, speed_limit * curvature_slow)
+        if abs(yaw_err) > math.radians(self.cfg.yaw_stop_threshold_deg):
+            if abs(yaw_err) < math.radians(self.cfg.turn_in_place_enter_deg):
+                cmd_vx = min(self.cfg.creep_cmd_vx, speed_limit)
+            else:
+                cmd_vx = 0.0
+        cmd[0] = cmd_vx * self.cfg.cmd_vx_scale
+        return cmd, None
+
+
+class NavGoodFollower(WaypointFollower):
+    """Follower shaped after sim2real nav_good/simple_nav_node.py.
+
+    This keeps the route as the active path, advances through near path points,
+    selects a lookahead target, then applies the same yaw gating and
+    turn-in-place structure as the ROS2 simple_nav node.
+    """
+
+    def __init__(self, waypoints: list[Waypoint], cfg: SimConfig) -> None:
+        super().__init__(waypoints, cfg)
+        self.goal_entered_tolerance = False
+        self.goal_complete_stable_count = 0
+        self.goal_exit_tolerance_margin = 0.05
+        self.final_align_kp_yaw_scale = 0.6
+        self.final_align_creep_speed = 0.03
+        self.path_reach_dist = 0.18
+        self.best_dist = float("inf")
+        self.last_progress_time = 0.0
+
+    def reset_timing(self, sim_time: float) -> None:
+        super().reset_timing(sim_time)
+        self.goal_entered_tolerance = False
+        self.goal_complete_stable_count = 0
+        self.turn_in_place = False
+        self.best_dist = float("inf")
+        self.last_progress_time = sim_time
+
+    def _current_goal(self) -> Waypoint | None:
+        return self.active
+
+    def _is_charge_segment(self, x: float, y: float, goal: Waypoint) -> bool:
+        policy = (goal.policy or "").lower()
+        if policy in {"charge", "wall", "obstacle", "climb"}:
+            return True
+        return -0.45 <= x <= 0.35 and -4.35 <= y <= -2.85
+
+    def _point_is_behind_segment(
+        self,
+        x: float,
+        y: float,
+        point: Waypoint,
+        next_point: Waypoint,
+    ) -> bool:
+        seg_x = next_point.x - point.x
+        seg_y = next_point.y - point.y
+        seg_len_sq = seg_x * seg_x + seg_y * seg_y
+        if seg_len_sq <= 1.0e-9:
+            return False
+        proj = ((x - point.x) * seg_x + (y - point.y) * seg_y) / seg_len_sq
+        return proj > 0.65 and math.hypot(next_point.x - x, next_point.y - y) < math.hypot(point.x - x, point.y - y)
+
+    def _advance_path_nodes(self, x: float, y: float, sim_time: float) -> str | None:
+        while self.index < len(self.waypoints) - 1:
+            goal = self.waypoints[self.index]
+            if goal.require_yaw:
+                break
+            next_goal = self.waypoints[self.index + 1]
+            tol = max(self.path_reach_dist, goal.tolerance if goal.tolerance is not None else 0.15)
+            dist = math.hypot(goal.x - x, goal.y - y)
+            if dist > tol and not self._point_is_behind_segment(x, y, goal, next_goal):
+                break
+            self.index += 1
+            self.wp_start_time = sim_time
+            self.turn_in_place = False
+            self.goal_entered_tolerance = False
+            self.goal_complete_stable_count = 0
+            self.best_dist = float("inf")
+            self.last_progress_time = sim_time
+        if self.index >= len(self.waypoints):
+            return "complete"
+        return None
+
+    def _path_follow_target(self, x: float, y: float, goal: Waypoint) -> Waypoint:
+        target_index = self.index
+        while target_index < len(self.waypoints) - 1:
+            candidate = self.waypoints[target_index + 1]
+            if math.hypot(candidate.x - x, candidate.y - y) > self.cfg.lookahead_m:
+                break
+            target_index += 1
+        return self.waypoints[target_index]
+
+    def update(self, x: float, y: float, yaw: float, sim_time: float) -> tuple[np.ndarray, str | None]:
+        nav_state = self._advance_path_nodes(x, y, sim_time)
+        if nav_state is not None:
+            return np.zeros(3, dtype=np.float32), nav_state
+
+        goal = self._current_goal()
+        if goal is None:
+            return np.zeros(3, dtype=np.float32), "complete"
+        if sim_time - self.wp_start_time > self.cfg.waypoint_timeout_s:
+            return np.zeros(3, dtype=np.float32), f"timeout at waypoint {goal.id}"
+
+        goal_dx = goal.x - x
+        goal_dy = goal.y - y
+        dist = math.hypot(goal_dx, goal_dy)
+        tol_enter = goal.tolerance if goal.tolerance is not None else 0.15
+        tol_exit = tol_enter + self.goal_exit_tolerance_margin
+        position_ready = dist < (tol_exit if self.goal_entered_tolerance else tol_enter)
+        max_vx = self.cfg.max_vx if goal.speed is None else min(self.cfg.max_vx, max(0.0, goal.speed))
+
+        if goal.require_yaw and position_ready:
+            self.goal_entered_tolerance = True
+            yaw_tolerance_deg = 5.0 if goal.yaw_tolerance_deg is None else max(0.0, goal.yaw_tolerance_deg)
+            yaw_err = self.normalize_angle(math.radians(goal.yaw_deg) - yaw)
+            if abs(yaw_err) > math.radians(yaw_tolerance_deg):
+                self.goal_complete_stable_count = 0
+                return np.array(
+                    [
+                        0.0,
+                        0.0,
+                        self.clamp(
+                            self.cfg.kp_yaw * self.final_align_kp_yaw_scale * yaw_err,
+                            -self.cfg.final_align_max_wz,
+                            self.cfg.final_align_max_wz,
+                        ),
+                    ],
+                    dtype=np.float32,
+                ), None
+            required_stable_cycles = (
+                self.cfg.stable_cycles if goal.stable_cycles is None else max(1, goal.stable_cycles)
+            )
+            self.goal_complete_stable_count += 1
+            if self.goal_complete_stable_count < required_stable_cycles:
+                return np.zeros(3, dtype=np.float32), None
+            self.index += 1
+            self.wp_start_time = sim_time
+            self.turn_in_place = False
+            self.goal_entered_tolerance = False
+            self.goal_complete_stable_count = 0
+            self.best_dist = float("inf")
+            self.last_progress_time = sim_time
+            if self.index >= len(self.waypoints):
+                return np.zeros(3, dtype=np.float32), "complete"
+            return np.zeros(3, dtype=np.float32), None
+
+        is_final_goal = self.index >= len(self.waypoints) - 1
+        if is_final_goal and position_ready:
+            self.goal_entered_tolerance = True
+            self.goal_complete_stable_count += 1
+            if self.goal_complete_stable_count < self.cfg.stable_cycles:
+                return np.zeros(3, dtype=np.float32), None
+            self.index += 1
+            self.wp_start_time = sim_time
+            self.turn_in_place = False
+            self.goal_entered_tolerance = False
+            self.goal_complete_stable_count = 0
+            if self.index >= len(self.waypoints):
+                return np.zeros(3, dtype=np.float32), "complete"
+            return np.zeros(3, dtype=np.float32), None
+
+        self.goal_entered_tolerance = False
+        self.goal_complete_stable_count = 0
+        if dist + self.cfg.stuck_progress_m < self.best_dist:
+            self.best_dist = dist
+            self.last_progress_time = sim_time
+        if sim_time - self.last_progress_time > self.cfg.waypoint_timeout_s:
+            return np.zeros(3, dtype=np.float32), f"no progress near waypoint {goal.id}"
+
+        target = goal if goal.require_yaw else self._path_follow_target(x, y, goal)
+        dx = target.x - x
+        dy = target.y - y
+        target_yaw = math.atan2(dy, dx)
+        yaw_err = self.normalize_angle(target_yaw - yaw)
+
+        if self.turn_in_place:
+            if abs(yaw_err) <= math.radians(self.cfg.turn_in_place_exit_deg):
+                self.turn_in_place = False
+        elif abs(yaw_err) >= math.radians(self.cfg.turn_in_place_enter_deg):
+            self.turn_in_place = True
+
+        if self.turn_in_place:
+            return np.array(
+                [
+                    0.0,
+                    0.0,
+                    self.clamp(self.cfg.kp_yaw * yaw_err, -self.cfg.turn_in_place_max_wz, self.cfg.turn_in_place_max_wz),
+                ],
+                dtype=np.float32,
+            ), None
+
+        cmd = np.zeros(3, dtype=np.float32)
+        cmd[2] = self.clamp(self.cfg.kp_yaw * yaw_err, -self.cfg.max_wz, self.cfg.max_wz)
+        if abs(yaw_err) <= math.radians(self.cfg.yaw_stop_threshold_deg):
+            path_dist = math.hypot(dx, dy)
+            if self._is_charge_segment(x, y, goal):
+                cmd[0] = max_vx
+            else:
+                cmd[0] = self.clamp(self.cfg.kp_dist * path_dist * math.cos(yaw_err), 0.0, max_vx)
+        cmd[0] *= self.cfg.cmd_vx_scale
+        return cmd, None
+
+
+class NavSim2SimFollower(WaypointFollower):
+    """Follower matched to sim2sim/nav_sim2sim.py NavController.
+
+    It tracks only the current waypoint, switches at 0.15 m, stops forward
+    motion above ~45 deg yaw error, and can keep full waypoint speed through
+    obstacle-charge segments instead of scaling speed down by distance.
+    """
+
+    def __init__(self, waypoints: list[Waypoint], cfg: SimConfig) -> None:
+        super().__init__(waypoints, cfg)
+        self.reach_dist = 0.15
+
+    def _advance_reached(self, x: float, y: float, sim_time: float) -> str | None:
+        while self.active is not None:
+            wp = self.active
+            tol = wp.tolerance if wp.tolerance is not None else self.reach_dist
+            tol = max(self.reach_dist, tol)
+            if math.hypot(wp.x - x, wp.y - y) >= tol:
+                break
+            if self.index < len(self.waypoints) - 1:
+                self.index += 1
+                self.turn_in_place = False
+                self.stable_count = 0
+                self.wp_start_time = sim_time
+                return None
+            self.index += 1
+            return "complete"
+        if self.index >= len(self.waypoints):
+            return "complete"
+        return None
+
+    def _is_charge_segment(self, x: float, y: float, wp: Waypoint) -> bool:
+        policy = (wp.policy or "").lower()
+        if policy in {"charge", "wall", "obstacle", "climb"}:
+            return True
+        # A.xml has a low wall/step around x=0, y=-3.5. nav_sim2sim keeps
+        # speed through comparable obstacle sections instead of distance taper.
+        return -0.45 <= x <= 0.35 and -4.35 <= y <= -2.85
+
+    def update(self, x: float, y: float, yaw: float, sim_time: float) -> tuple[np.ndarray, str | None]:
+        nav_state = self._advance_reached(x, y, sim_time)
+        if nav_state is not None:
+            return np.zeros(3, dtype=np.float32), nav_state
+        wp = self.active
+        if wp is None:
+            return np.zeros(3, dtype=np.float32), "complete"
+        if sim_time - self.wp_start_time > self.cfg.waypoint_timeout_s:
+            return np.zeros(3, dtype=np.float32), f"timeout at waypoint {wp.id}"
+
+        dx = wp.x - x
+        dy = wp.y - y
+        dist = math.hypot(dx, dy)
+        target_yaw = math.atan2(dy, dx)
+        yaw_err = self.normalize_angle(target_yaw - yaw)
+        speed_limit = self.cfg.max_vx if wp.speed is None else min(self.cfg.max_vx, max(0.0, wp.speed))
+
+        cmd = np.zeros(3, dtype=np.float32)
+        cmd[2] = self.clamp(self.cfg.kp_yaw * yaw_err, -self.cfg.max_wz, self.cfg.max_wz)
+        if abs(yaw_err) > 0.8:
+            cmd[0] = 0.0
+        elif self._is_charge_segment(x, y, wp):
+            cmd[0] = speed_limit
+        else:
+            cmd[0] = self.clamp(self.cfg.kp_dist * dist * math.cos(yaw_err), -0.2, speed_limit)
+        cmd[0] *= self.cfg.cmd_vx_scale
+        return cmd, None
+
+
+class SlalomScriptFollower(NavGoodFollower):
+    """NavGood follower plus the sim2real slalomStraight odometry script.
+
+    The script starts only after the robot reaches the first slalomStraight
+    point, then drives consecutive slalomStraight points with body-frame
+    vx/vy/wz feedback. This mirrors simple_nav_node.py instead of the older
+    pure waypoint followers.
+    """
+
+    def __init__(self, waypoints: list[Waypoint], cfg: SimConfig) -> None:
+        super().__init__(waypoints, cfg)
+        self.slalom_script_active = False
+        self.slalom_script_start_index = 0
+        self.slalom_script_end_index = 0
+        self.slalom_script_steps: list[SlalomScriptStep] = []
+        self.slalom_script_step_index = 0
+        self.slalom_script_step_stable_count = 0
+
+    def reset_timing(self, sim_time: float) -> None:
+        super().reset_timing(sim_time)
+        self.reset_slalom_script()
+
+    def reset_slalom_script(self) -> None:
+        self.slalom_script_active = False
+        self.slalom_script_start_index = 0
+        self.slalom_script_end_index = 0
+        self.slalom_script_steps = []
+        self.slalom_script_step_index = 0
+        self.slalom_script_step_stable_count = 0
+
+    @staticmethod
+    def _is_slalom_script_waypoint(wp: Waypoint | None) -> bool:
+        return bool(wp is not None and getattr(wp, "slalom_straight", False))
+
+    def _advance_path_nodes(self, x: float, y: float, sim_time: float) -> str | None:
+        active = self.active
+        if (
+            self.cfg.slalom_script_enabled
+            and self._is_slalom_script_waypoint(active)
+            and not self.slalom_script_active
+            and self.index + 1 < len(self.waypoints)
+            and self._is_slalom_script_waypoint(self.waypoints[self.index + 1])
+        ):
+            # Do not let the normal path follower skip the script start point at
+            # the wider path tolerance; the real node gates this with
+            # nav_slalom_script_start_tolerance.
+            return None
+        return super()._advance_path_nodes(x, y, sim_time)
+
+    def get_slalom_script_bounds(self, start_index: int) -> tuple[int, int]:
+        if start_index < 0 or start_index >= len(self.waypoints):
+            return start_index, start_index
+        if not self._is_slalom_script_waypoint(self.waypoints[start_index]):
+            return start_index, start_index
+        end_index = start_index
+        while end_index + 1 < len(self.waypoints):
+            if getattr(self.waypoints[end_index], "slalom_script_break", False):
+                break
+            if not self._is_slalom_script_waypoint(self.waypoints[end_index + 1]):
+                break
+            end_index += 1
+        return start_index, end_index
+
+    def build_slalom_script_steps(self, start_index: int, end_index: int) -> list[SlalomScriptStep]:
+        if end_index <= start_index:
+            return []
+
+        steps: list[SlalomScriptStep] = []
+        for index in range(start_index, end_index):
+            goal = self.waypoints[index]
+            next_goal = self.waypoints[index + 1]
+            route_dx = next_goal.x - goal.x
+            route_dy = next_goal.y - goal.y
+            route_dist = math.hypot(route_dx, route_dy)
+            route_yaw = math.atan2(route_dy, route_dx) if route_dist > 1.0e-6 else math.radians(goal.yaw_deg)
+            yaw_source = self.cfg.slalom_script_drive_yaw_source
+            if yaw_source == "segment":
+                target_yaw = route_yaw
+            elif yaw_source == "blend":
+                target_yaw = route_yaw
+                if index + 2 <= end_index:
+                    following_goal = self.waypoints[index + 2]
+                    next_dx = following_goal.x - next_goal.x
+                    next_dy = following_goal.y - next_goal.y
+                    if math.hypot(next_dx, next_dy) > 1.0e-6:
+                        next_yaw = math.atan2(next_dy, next_dx)
+                        delta = self.normalize_angle(next_yaw - route_yaw)
+                        target_yaw = route_yaw + 0.15 * delta
+            elif yaw_source == "next":
+                target_yaw = math.radians(next_goal.yaw_deg)
+            else:
+                target_yaw = math.radians(goal.yaw_deg)
+            target_yaw = self.normalize_angle(target_yaw)
+            if self.cfg.slalom_script_rotate_steps_enabled:
+                steps.append(
+                    SlalomScriptStep(
+                        kind="rotate",
+                        start_index=index,
+                        end_index=index,
+                        target_x=goal.x,
+                        target_y=goal.y,
+                        target_yaw=target_yaw,
+                        pos_tolerance=getattr(goal, "slalom_script_pos_tolerance", None),
+                    )
+                )
+            if route_dist >= self.cfg.slalom_script_min_step_distance:
+                forward = math.cos(target_yaw) * route_dx + math.sin(target_yaw) * route_dy
+                left = -math.sin(target_yaw) * route_dx + math.cos(target_yaw) * route_dy
+                steps.append(
+                    SlalomScriptStep(
+                        kind="drive",
+                        start_index=index,
+                        end_index=index + 1,
+                        target_x=next_goal.x,
+                        target_y=next_goal.y,
+                        target_yaw=target_yaw,
+                        pos_tolerance=getattr(next_goal, "slalom_script_pos_tolerance", None),
+                        forward=forward,
+                        left=left,
+                    )
+                )
+
+        final_goal = self.waypoints[end_index]
+        if self.cfg.slalom_script_final_rotate_enabled:
+            steps.append(
+                SlalomScriptStep(
+                    kind="rotate",
+                    start_index=end_index,
+                    end_index=end_index,
+                    target_x=final_goal.x,
+                    target_y=final_goal.y,
+                    target_yaw=math.radians(final_goal.yaw_deg),
+                    pos_tolerance=getattr(final_goal, "slalom_script_pos_tolerance", None),
+                )
+            )
+        return steps
+
+    def maybe_start_slalom_script(self, x: float, y: float, sim_time: float) -> bool:
+        if not self.cfg.slalom_script_enabled or self.slalom_script_active:
+            return self.slalom_script_active
+        active = self.active
+        if not self._is_slalom_script_waypoint(active):
+            return False
+        if math.hypot(active.x - x, active.y - y) > self.cfg.slalom_script_start_tolerance:
+            return False
+
+        start_index, end_index = self.get_slalom_script_bounds(self.index)
+        if end_index <= start_index:
+            return False
+        steps = self.build_slalom_script_steps(start_index, end_index)
+        if not steps:
+            return False
+
+        self.slalom_script_active = True
+        self.slalom_script_start_index = start_index
+        self.slalom_script_end_index = end_index
+        self.slalom_script_steps = steps
+        self.slalom_script_step_index = 0
+        self.slalom_script_step_stable_count = 0
+        self.turn_in_place = False
+        self.goal_entered_tolerance = False
+        self.goal_complete_stable_count = 0
+        self.wp_start_time = sim_time
+        return True
+
+    def compute_slalom_script_command(
+        self,
+        step: SlalomScriptStep,
+        x: float,
+        y: float,
+        yaw: float,
+    ) -> tuple[np.ndarray, bool]:
+        cmd = np.zeros(3, dtype=np.float32)
+        target_yaw = step.target_yaw
+        yaw_tolerance = math.radians(self.cfg.slalom_script_yaw_tolerance_deg)
+
+        if step.kind == "rotate":
+            if target_yaw is None:
+                return cmd, True
+            yaw_err = self.normalize_angle(target_yaw - yaw)
+            if abs(yaw_err) <= yaw_tolerance:
+                return cmd, True
+            cmd[2] = self.clamp(
+                self.cfg.slalom_script_kp_yaw * yaw_err,
+                -self.cfg.slalom_script_max_wz,
+                self.cfg.slalom_script_max_wz,
+            )
+            cmd[2] = self.apply_min_command(
+                float(cmd[2]),
+                self.cfg.slalom_script_min_cmd_angular,
+                self.cfg.slalom_script_max_wz,
+                self.cfg.slalom_script_min_cmd_epsilon,
+            )
+            return cmd, False
+
+        dx = step.target_x - x
+        dy = step.target_y - y
+        if target_yaw is None:
+            target_yaw = math.atan2(dy, dx) if math.hypot(dx, dy) > 1.0e-6 else yaw
+        yaw_err = self.normalize_angle(target_yaw - yaw)
+        pos_err = math.hypot(dx, dy)
+        pos_tolerance = (
+            self.cfg.slalom_script_pos_tolerance
+            if step.pos_tolerance is None
+            else max(0.01, float(step.pos_tolerance))
+        )
+        complete = (
+            pos_err <= pos_tolerance
+            and (
+                not self.cfg.slalom_script_require_yaw_at_step
+                or abs(yaw_err) <= yaw_tolerance
+            )
+        )
+        if complete:
+            return cmd, True
+
+        cmd[2] = self.clamp(
+            self.cfg.slalom_script_kp_yaw * yaw_err,
+            -self.cfg.slalom_script_max_wz,
+            self.cfg.slalom_script_max_wz,
+        )
+        cmd[2] = self.apply_min_command(
+            float(cmd[2]),
+            self.cfg.slalom_script_min_cmd_angular,
+            self.cfg.slalom_script_max_wz,
+            self.cfg.slalom_script_min_cmd_epsilon,
+        )
+        if abs(yaw_err) <= math.radians(self.cfg.slalom_script_yaw_gate_deg):
+            err_forward = math.cos(yaw) * dx + math.sin(yaw) * dy
+            err_left = -math.sin(yaw) * dx + math.cos(yaw) * dy
+            yaw_scale = max(0.35, math.cos(yaw_err) ** 2)
+            curvature_scale = 1.0
+            if self.cfg.slalom_script_curvature_speed_enabled:
+                gate = max(1.0e-6, math.radians(self.cfg.slalom_script_yaw_gate_deg))
+                curvature_scale = max(
+                    self.cfg.slalom_script_curvature_min_scale,
+                    1.0 - abs(yaw_err) / gate,
+                )
+            cmd[0] = self.clamp(
+                self.cfg.slalom_script_kp_dist * err_forward,
+                -self.cfg.slalom_script_max_vx * yaw_scale * curvature_scale,
+                self.cfg.slalom_script_max_vx * yaw_scale * curvature_scale,
+            )
+            cmd[1] = self.clamp(
+                self.cfg.slalom_script_kp_dist * err_left,
+                -self.cfg.slalom_script_max_vy * curvature_scale,
+                self.cfg.slalom_script_max_vy * curvature_scale,
+            )
+            cmd[0] = self.apply_min_command(
+                float(cmd[0]),
+                self.cfg.slalom_script_min_cmd_linear,
+                self.cfg.slalom_script_max_vx,
+                self.cfg.slalom_script_min_cmd_epsilon,
+            )
+            cmd[1] = self.apply_min_command(
+                float(cmd[1]),
+                self.cfg.slalom_script_min_cmd_linear,
+                self.cfg.slalom_script_max_vy,
+                self.cfg.slalom_script_min_cmd_epsilon,
+            )
+        return cmd, False
+
+    @classmethod
+    def apply_min_command(
+        cls,
+        value: float,
+        min_abs: float,
+        max_abs: float,
+        epsilon: float,
+    ) -> float:
+        if max_abs <= 0.0 or min_abs <= 0.0:
+            return value
+        abs_value = abs(value)
+        if abs_value < max(0.0, epsilon) or abs_value >= min_abs:
+            return value
+        return cls.clamp(math.copysign(min_abs, value), -max_abs, max_abs)
+
+    def finish_slalom_script(self, sim_time: float) -> tuple[np.ndarray, str | None]:
+        end_index = self.slalom_script_end_index
+        self.reset_slalom_script()
+        self.index = end_index + 1
+        self.wp_start_time = sim_time
+        self.turn_in_place = False
+        self.goal_entered_tolerance = False
+        self.goal_complete_stable_count = 0
+        self.best_dist = float("inf")
+        self.last_progress_time = sim_time
+        if self.index >= len(self.waypoints):
+            return np.zeros(3, dtype=np.float32), "complete"
+        return np.zeros(3, dtype=np.float32), None
+
+    def handle_slalom_script(
+        self,
+        x: float,
+        y: float,
+        yaw: float,
+        sim_time: float,
+    ) -> tuple[np.ndarray, str | None] | None:
+        if not self.slalom_script_active and not self.maybe_start_slalom_script(x, y, sim_time):
+            return None
+        if sim_time - self.wp_start_time > self.cfg.waypoint_timeout_s:
+            active = self.active
+            active_id = active.id if active is not None else "unknown"
+            return np.zeros(3, dtype=np.float32), (
+                f"timeout at slalom step {self.slalom_script_step_index + 1}/"
+                f"{len(self.slalom_script_steps)} near waypoint {active_id}"
+            )
+
+        while self.slalom_script_step_index < len(self.slalom_script_steps):
+            step = self.slalom_script_steps[self.slalom_script_step_index]
+            cmd, complete = self.compute_slalom_script_command(step, x, y, yaw)
+            if complete:
+                self.slalom_script_step_stable_count += 1
+                if self.slalom_script_step_stable_count >= self.cfg.slalom_script_stable_cycles:
+                    self.slalom_script_step_index += 1
+                    self.index = max(self.index, min(step.end_index, self.slalom_script_end_index))
+                    self.wp_start_time = sim_time
+                    self.slalom_script_step_stable_count = 0
+                    continue
+            else:
+                self.slalom_script_step_stable_count = 0
+            return cmd, None
+
+        return self.finish_slalom_script(sim_time)
+
+    def update(self, x: float, y: float, yaw: float, sim_time: float) -> tuple[np.ndarray, str | None]:
+        script_result = self.handle_slalom_script(x, y, yaw, sim_time)
+        if script_result is not None:
+            return script_result
+        return super().update(x, y, yaw, sim_time)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run MuJoCo+ONNX route validation.")
+    parser.add_argument("--terrain-xml", type=Path, default=PROJECT_ROOT / "tools/nav_tools/xml/A.xml")
+    parser.add_argument("--points", type=Path, default=PROJECT_ROOT / "tools/nav_tools/points/points_20260705_174627.json")
+    parser.add_argument("--onnx", type=Path, default=PROJECT_ROOT / "model_6800.onnx")
+    parser.add_argument("--crawl-onnx", type=Path, default=PROJECT_ROOT / "model_crawl.onnx")
+    parser.add_argument(
+        "--policy-backend",
+        choices=("policy-runner", "onnx", "rough-ik-crawl"),
+        default="policy-runner",
+        help="policy-runner matches nav_sim2sim.py; rough-ik-crawl matches sim2real nav_good crawl_backend=ik.",
+    )
+    parser.add_argument("--enable-policy-switch", action="store_true")
+    parser.add_argument(
+        "--mission",
+        choices=("json", "slalom", "grand"),
+        default="json",
+        help="json uses --points; slalom/grand use the hardcoded nav_sim2sim coordinate frame.",
+    )
+    parser.add_argument("--robot-xml", type=Path, default=PROJECT_ROOT / "mjcf/wheelleg.xml")
+    parser.add_argument("--hfield-dir", type=Path, default=PROJECT_ROOT / "sim2sim/terrain")
+    parser.add_argument("--out-dir", type=Path, default=PROJECT_ROOT / "sim2sim/route_check_runs")
+    parser.add_argument("--avoid-margin", type=float, default=0.05)
+    parser.add_argument("--footprint-radius", type=float, default=None)
+    parser.add_argument("--max-time", type=float, default=420.0)
+    parser.add_argument("--waypoint-timeout", type=float, default=30.0)
+    parser.add_argument("--max-vx", type=float, default=1.2)
+    parser.add_argument("--max-vy", type=float, default=0.0)
+    parser.add_argument("--max-wz", type=float, default=0.8)
+    parser.add_argument("--kp-dist", type=float, default=0.8)
+    parser.add_argument("--kp-yaw", type=float, default=1.8)
+    parser.add_argument("--yaw-stop-threshold-deg", type=float, default=45.0)
+    parser.add_argument("--turn-in-place-enter-deg", type=float, default=70.0)
+    parser.add_argument("--turn-in-place-exit-deg", type=float, default=18.0)
+    parser.add_argument("--follower", choices=("waypoint", "pure-pursuit", "nav-good", "nav-sim2sim", "slalom-script", "nav-script"), default="pure-pursuit")
+    parser.add_argument("--lookahead", type=float, default=0.45)
+    parser.add_argument("--min-cmd-vx", type=float, default=0.08)
+    parser.add_argument("--creep-cmd-vx", type=float, default=0.04)
+    parser.add_argument("--cmd-vx-scale", type=float, default=1.0)
+    parser.add_argument("--stuck-timeout", type=float, default=4.0)
+    parser.add_argument("--recovery-duration", type=float, default=2.0)
+    parser.add_argument("--max-recoveries", type=int, default=4)
+    parser.add_argument("--slalom-script", action="store_true", default=True)
+    parser.add_argument("--no-slalom-script", dest="slalom_script", action="store_false")
+    parser.add_argument("--slalom-script-start-tolerance", type=float, default=0.10)
+    parser.add_argument("--slalom-script-pos-tolerance", type=float, default=0.08)
+    parser.add_argument("--slalom-script-yaw-tolerance-deg", type=float, default=5.0)
+    parser.add_argument("--slalom-script-stable-cycles", type=int, default=1)
+    parser.add_argument("--slalom-script-rotate-steps", action="store_true", default=False)
+    parser.add_argument("--slalom-script-final-rotate", action="store_true", default=False)
+    parser.add_argument("--slalom-script-require-yaw-at-step", action="store_true", default=False)
+    parser.add_argument("--slalom-script-kp-dist", type=float, default=1.0)
+    parser.add_argument("--slalom-script-kp-yaw", type=float, default=1.6)
+    parser.add_argument("--slalom-script-max-vx", type=float, default=0.60)
+    parser.add_argument("--slalom-script-max-vy", type=float, default=0.50)
+    parser.add_argument("--slalom-script-max-wz", type=float, default=0.50)
+    parser.add_argument("--slalom-script-min-cmd-linear", type=float, default=0.2)
+    parser.add_argument("--slalom-script-min-cmd-angular", type=float, default=0.0)
+    parser.add_argument("--slalom-script-min-cmd-epsilon", type=float, default=0.05)
+    parser.add_argument("--slalom-script-min-step-distance", type=float, default=0.02)
+    parser.add_argument("--slalom-script-yaw-gate-deg", type=float, default=25.0)
+    parser.add_argument("--slalom-script-curvature-speed", action="store_true", default=True)
+    parser.add_argument(
+        "--no-slalom-script-curvature-speed",
+        dest="slalom_script_curvature_speed",
+        action="store_false",
+    )
+    parser.add_argument("--slalom-script-curvature-min-scale", type=float, default=0.55)
+    parser.add_argument(
+        "--slalom-script-drive-yaw-source",
+        choices=("current", "next", "segment", "blend"),
+        default="segment",
+        help="Yaw used while driving each slalom segment.",
+    )
+    parser.add_argument("--local-safety", action="store_true", default=False)
+    parser.add_argument("--no-local-safety", dest="local_safety", action="store_false")
+    parser.add_argument("--safety-horizon", type=float, default=1.0)
+    parser.add_argument("--safety-dt", type=float, default=0.1)
+    parser.add_argument(
+        "--start-yaw-offset-deg",
+        type=float,
+        default=0.0,
+        help="Added to route yawDeg when placing the robot.",
+    )
+    parser.add_argument(
+        "--heading-offset-deg",
+        type=float,
+        default=0.0,
+        help="Added to MuJoCo base yaw before waypoint heading control.",
+    )
+    parser.add_argument("--sample-every", type=int, default=5)
+    parser.add_argument("--sim-speed", type=float, default=1.0, help="Simulation wall-clock speed factor, matching nav_sim2sim.py timing semantics.")
+    parser.add_argument("--viewer", action="store_true", help="Open MuJoCo passive viewer while the check runs.")
+    parser.add_argument("--viewer-sync-every", type=int, default=2)
+    parser.add_argument("--dashboard", action="store_true", help="Open a pygame telemetry dashboard with route, avoid regions, and trail.")
+    parser.add_argument("--dashboard-fps", type=float, default=30.0)
+    parser.add_argument("--dashboard-sim-speed", type=float, default=1.0, help="Initial dashboard simulation speed factor; can be changed in the UI up to 10x.")
+    parser.add_argument("--dashboard-width", type=int, default=1180)
+    parser.add_argument("--dashboard-height", type=int, default=760)
+    parser.add_argument("--dashboard-trail", type=int, default=6000)
+    parser.add_argument("--start-index", type=int, default=1, help="1-based waypoint index to start testing from.")
+    parser.add_argument("--end-index", type=int, default=None, help="1-based waypoint index to stop testing at.")
+    parser.add_argument("--start-id", type=str, default=None, help="Waypoint id to start testing from; overrides --start-index.")
+    parser.add_argument("--end-id", type=str, default=None, help="Waypoint id to stop testing at; overrides --end-index.")
+    parser.add_argument(
+        "--skip-initial-waypoint",
+        action="store_true",
+        help="Use the first sliced waypoint as spawn pose, then navigate to the next waypoint.",
+    )
+    parser.add_argument(
+        "--auto-skip-slice-start",
+        action="store_true",
+        default=True,
+        help="When --start-index > 1, skip the sliced start waypoint by default.",
+    )
+    parser.add_argument("--no-auto-skip-slice-start", dest="auto_skip_slice_start", action="store_false")
+    parser.add_argument("--start-z", type=float, default=0.75, help="Initial base z before settling.")
+    parser.add_argument("--start-x-offset", type=float, default=0.0, help="World x offset added to the initial spawn pose.")
+    parser.add_argument("--start-y-offset", type=float, default=0.0, help="World y offset added to the initial spawn pose.")
+    parser.add_argument("--settle-steps", type=int, default=500)
+    parser.add_argument("--min-base-z", type=float, default=0.12)
+    parser.add_argument("--stop-on-clearance-violation", action="store_true")
+    parser.add_argument("--deployment-randomization", action="store_true")
+    parser.add_argument("--random-seed", type=int, default=1)
+    parser.add_argument("--pose-noise-xy", type=float, default=0.015)
+    parser.add_argument("--pose-noise-yaw-deg", type=float, default=1.0)
+    parser.add_argument("--command-delay-ms", type=float, default=40.0)
+    parser.add_argument("--action-delay-ms", type=float, default=20.0)
+    parser.add_argument("--control-jitter-ms", type=float, default=4.0)
+    parser.add_argument("--command-dropout-prob", type=float, default=0.01)
+    parser.add_argument("--push-force", type=float, default=25.0)
+    parser.add_argument("--push-interval", type=float, default=8.0)
+    parser.add_argument("--push-duration", type=float, default=0.08)
+    parser.add_argument("--no-csv", action="store_true")
+    return parser.parse_args()
+
+
+def make_combined_terrain_xml(terrain_xml: Path, work_dir: Path) -> Path:
+    text = terrain_xml.read_text(encoding="utf-8")
+    if '<include file="go2w.xml"/>' not in text and '<include file="go2w.xml" />' not in text:
+        end = text.find(">")
+        if end < 0:
+            raise ValueError(f"Invalid terrain XML: {terrain_xml}")
+        text = text[: end + 1] + '\n<include file="go2w.xml"/>' + text[end + 1 :]
+    out_path = work_dir / f"{terrain_xml.stem}_with_robot_include.xml"
+    out_path.write_text(text, encoding="utf-8")
+    return out_path
+
+
+def builtin_mission_waypoints(name: str) -> list[Waypoint]:
+    slalom = [
+        (2.9, -9.5, 0.4, "rough"),
+        (1.8, -9.95, 0.4, "rough"),
+        (0.9, -10.0, 0.4, "rough"),
+        (0.8, -10.8, 0.4, "rough"),
+        (0.9, -11.0, 0.4, "rough"),
+        (2.7, -11.0, 0.4, "rough"),
+        (2.8, -11.8, 0.4, "rough"),
+        (2.7, -12.0, 0.4, "rough"),
+        (0.9, -12.0, 0.4, "rough"),
+        (0.9, -12.75, 0.4, "rough"),
+        (1.42, -12.82, 0.4, "rough"),
+        (2.3, -13.3, 0.4, "rough"),
+        (2.3, -11.5, 0.4, "rough"),
+        (3.32, -12.44, 0.4, "rough"),
+        (3.7, -9.0, 0.6, "rough"),
+    ]
+    grand_tail = [
+        (5.7, -10.2, 0.5, "rough"),
+        (5.7, -9.0, 0.3, "crawl"),
+        (5.7, -7.8, 0.3, "rough"),
+        (5.7, -7.0, 0.5, "rough"),
+        (3.7, -9.0, 0.5, "rough"),
+        (3.7, -12.5, 0.5, "rough"),
+        (4.84, -12.5, 0.4, "rough"),
+        (5.84, -12.0, 0.4, "rough"),
+        (5.84, -10.2, 0.4, "rough"),
+        (3.3, -10.2, 0.5, "rough"),
+        (3.3, -9.0, 0.5, "rough"),
+        (3.7, -9.0, 0.5, "rough"),
+        (3.3, -9.0, 0.5, "rough"),
+        (1.8, -9.0, 0.5, "rough"),
+        (1.8, -8.0, 0.4, "rough"),
+        (1.8, -7.5, 0.4, "rough"),
+        (1.8, -5.8, 1.2, "rough"),
+        (1.8, -3.5, 0.4, "rough"),
+        (1.8, -1.6, 0.4, "rough"),
+        (1.8, -1.38, 0.4, "rough"),
+        (1.8, 0.0, 0.4, "rough"),
+        (3.2, 0.0, 0.4, "rough"),
+        (4.85, 0.0, 0.4, "rough"),
+        (5.7, -0.5, 0.4, "rough"),
+        (5.7, -2.25, 0.4, "rough"),
+        (5.7, -3.5, 0.4, "rough"),
+        (5.7, -5.0, 0.4, "rough"),
+        (5.7, -7.0, 0.4, "rough"),
+        (3.3, -7.0, 0.5, "rough"),
+        (3.3, -9.0, 0.5, "rough"),
+        (3.7, -9.0, 0.6, "rough"),
+    ]
+    rows = slalom if name == "slalom" else slalom + grand_tail
+    return [
+        Waypoint(
+            index=i,
+            id=f"{name}_{i}",
+            x=x,
+            y=y,
+            yaw_deg=0.0,
+            speed=speed,
+            policy=policy,
+            tolerance=0.15,
+        )
+        for i, (x, y, speed, policy) in enumerate(rows, start=1)
+    ]
+
+
+def load_route(path: Path) -> tuple[list[Waypoint], list[AvoidRegion]]:
+    with path.open("r", encoding="utf-8") as f:
+        payload = json.load(f)
+    if not isinstance(payload, dict):
+        raise ValueError(f"Route JSON root must be object: {path}")
+    return load_waypoints(payload), load_regions(payload)
+
+
+def load_requested_route(args: argparse.Namespace) -> tuple[list[Waypoint], list[AvoidRegion]]:
+    if args.mission == "json":
+        waypoints, regions = load_route(args.points)
+    else:
+        waypoints, regions = builtin_mission_waypoints(args.mission), []
+
+    id_to_pos = {wp.id: i + 1 for i, wp in enumerate(waypoints)}
+    start = id_to_pos.get(str(args.start_id), max(1, int(args.start_index))) if args.start_id is not None else max(1, int(args.start_index))
+    end_default = int(args.end_index) if args.end_index is not None else len(waypoints)
+    end = id_to_pos.get(str(args.end_id), end_default) if args.end_id is not None else end_default
+    if start > end or start > len(waypoints):
+        raise ValueError(f"Invalid waypoint slice: start={start} end={end} total={len(waypoints)}")
+    sliced = waypoints[start - 1 : min(end, len(waypoints))]
+    if len(sliced) < 1:
+        raise ValueError("Waypoint slice is empty")
+    return sliced, regions
+
+
+def initial_pose_for_route(args: argparse.Namespace, waypoints: list[Waypoint]) -> tuple[float, float, float]:
+    if args.mission == "json":
+        start = waypoints[0]
+        return start.x, start.y, start.yaw_deg + float(args.start_yaw_offset_deg)
+    return 3.7, -9.0, 0.0
+
+
+def set_robot_pose(
+    io: MuJoCoIO,
+    x: float,
+    y: float,
+    yaw_deg: float,
+    z: float,
+    settle_steps: int,
+    default_dof_pos: np.ndarray,
+) -> None:
+    yaw = math.radians(yaw_deg)
+    io.d.qpos[0] = x
+    io.d.qpos[1] = y
+    io.d.qpos[2] = z
+    io.d.qpos[3:7] = [math.cos(yaw * 0.5), 0.0, 0.0, math.sin(yaw * 0.5)]
+    io.d.qpos[io.qpos_ids] = default_dof_pos
+    io.d.qvel[:] = 0.0
+    io.d.ctrl[io.ctrl_ids[:12]] = default_dof_pos[:12]
+    io.d.ctrl[io.ctrl_ids[12:]] = 0.0
+    mujoco.mj_forward(io.m, io.d)
+    for _ in range(max(0, int(settle_steps))):
+        mujoco.mj_step(io.m, io.d)
+
+
+def clearance_to_regions(
+    x: float,
+    y: float,
+    regions: list[AvoidRegion],
+) -> tuple[float, str]:
+    best = float("inf")
+    best_name = ""
+    for region in regions:
+        polygon = region.polygon
+        if len(polygon) < 3:
+            continue
+        if point_in_polygon(x, y, polygon):
+            return 0.0, region.name
+        dist = min(
+            point_segment_distance(x, y, ax, ay, bx, by)
+            for (ax, ay), (bx, by) in zip(polygon, polygon[1:] + polygon[:1])
+        )
+        if dist < best:
+            best = dist
+            best_name = region.name
+    return best, best_name
+
+
+def safety_filter_command(
+    desired: np.ndarray,
+    x: float,
+    y: float,
+    yaw: float,
+    target: Waypoint | None,
+    regions: list[AvoidRegion],
+    required_clearance: float,
+    cfg: SimConfig,
+    horizon_s: float,
+    dt_s: float,
+) -> np.ndarray:
+    if not regions or target is None:
+        return desired
+
+    base_vx = float(desired[0])
+    base_vy = float(desired[1])
+    base_wz = float(desired[2])
+    vx_abs = abs(base_vx)
+    vy_abs = abs(base_vy)
+    vx_sign = -1.0 if base_vx < 0.0 else 1.0
+    vy_sign = -1.0 if base_vy < 0.0 else 1.0
+    vx_samples = [0.0, 0.35 * vx_abs, 0.65 * vx_abs, vx_abs]
+    vy_samples = [0.0, 0.5 * vy_abs, vy_abs]
+    wz_span = max(0.35, min(cfg.max_wz, abs(base_wz) + 0.35))
+    wz_samples = [
+        base_wz - wz_span,
+        base_wz - 0.5 * wz_span,
+        base_wz,
+        base_wz + 0.5 * wz_span,
+        base_wz + wz_span,
+    ]
+    best_cmd = desired.copy()
+    best_score = -float("inf")
+    initial_dist = math.hypot(target.x - x, target.y - y)
+    steps = max(1, int(round(horizon_s / dt_s)))
+
+    for vx_mag in vx_samples:
+        for vy_mag in vy_samples:
+            for wz in wz_samples:
+                px, py, pyaw = x, y, yaw
+                min_margin = float("inf")
+                for _ in range(steps):
+                    vx = vx_sign * vx_mag
+                    vy = vy_sign * vy_mag
+                    px += (vx * math.cos(pyaw) - vy * math.sin(pyaw)) * dt_s
+                    py += (vx * math.sin(pyaw) + vy * math.cos(pyaw)) * dt_s
+                    pyaw = WaypointFollower.normalize_angle(pyaw + wz * dt_s)
+                    clearance, _ = clearance_to_regions(px, py, regions)
+                    min_margin = min(min_margin, clearance - required_clearance)
+                final_dist = math.hypot(target.x - px, target.y - py)
+                progress = initial_dist - final_dist
+                yaw_cost = abs(wz - base_wz)
+                speed_cost = abs(vx_mag - vx_abs) + abs(vy_mag - vy_abs)
+                risk_penalty = 20.0 * max(0.0, -min_margin)
+                margin_reward = min(0.4, min_margin)
+                score = 3.0 * progress + margin_reward - risk_penalty - 0.12 * yaw_cost - 0.08 * speed_cost
+                if score > best_score:
+                    best_score = score
+                    best_cmd = np.array(
+                        [
+                            vx_sign * vx_mag,
+                            vy_sign * vy_mag,
+                            WaypointFollower.clamp(wz, -cfg.max_wz, cfg.max_wz),
+                        ],
+                        dtype=np.float32,
+                    )
+    return best_cmd
+
+
+def robot_yaw_from_qpos(qpos: np.ndarray) -> float:
+    qw, qx, qy, qz = qpos[3:7]
+    siny_cosp = 2.0 * (qw * qz + qx * qy)
+    cosy_cosp = 1.0 - 2.0 * (qy * qy + qz * qz)
+    return math.atan2(siny_cosp, cosy_cosp)
+
+
+def quat_to_euler_wxyz(quat: np.ndarray) -> tuple[float, float, float]:
+    qw, qx, qy, qz = [float(v) for v in quat]
+    sinr_cosp = 2.0 * (qw * qx + qy * qz)
+    cosr_cosp = 1.0 - 2.0 * (qx * qx + qy * qy)
+    roll = math.atan2(sinr_cosp, cosr_cosp)
+    sinp = 2.0 * (qw * qy - qz * qx)
+    pitch = math.asin(max(-1.0, min(1.0, sinp)))
+    yaw = robot_yaw_from_qpos(np.array([0.0, 0.0, 0.0, qw, qx, qy, qz], dtype=np.float64))
+    return roll, pitch, yaw
+
+
+@dataclass(frozen=True)
+class DashboardGeom:
+    name: str
+    kind: str
+    pos: tuple[float, ...]
+    size: tuple[float, ...]
+    quat: tuple[float, float, float, float]
+    rgba: tuple[float, float, float, float]
+    collidable: bool
+
+
+def parse_dashboard_geoms(xml_path: Path) -> list[DashboardGeom]:
+    geoms: list[DashboardGeom] = []
+    if not xml_path.exists():
+        return geoms
+    try:
+        root = ET.parse(xml_path).getroot()
+    except ET.ParseError:
+        return geoms
+    for index, geom in enumerate(root.iter("geom"), start=1):
+        name = geom.get("name", f"geom_{index}")
+        if name == "floor":
+            continue
+        kind = geom.get("type", "box")
+        pos_text = geom.get("pos")
+        size_text = geom.get("size")
+        if not pos_text or not size_text:
+            continue
+        try:
+            pos = tuple(float(v) for v in pos_text.split())
+            size = tuple(float(v) for v in size_text.split())
+            quat_values = tuple(float(v) for v in geom.get("quat", "1 0 0 0").split())
+            rgba_values = tuple(float(v) for v in geom.get("rgba", "0.65 0.65 0.65 1").split())
+        except ValueError:
+            continue
+        if len(pos) < 2 or len(size) < 1 or len(quat_values) != 4:
+            continue
+        rgba = rgba_values if len(rgba_values) == 4 else (0.65, 0.65, 0.65, 1.0)
+        collidable = geom.get("contype", "1") != "0" and geom.get("conaffinity", "1") != "0"
+        geoms.append(
+            DashboardGeom(
+                name=name,
+                kind=kind,
+                pos=pos,
+                size=size,
+                quat=quat_values,  # type: ignore[arg-type]
+                rgba=rgba,  # type: ignore[arg-type]
+                collidable=collidable,
+            )
+        )
+    return geoms
+
+
+def quat_yaw_wxyz(quat: tuple[float, float, float, float]) -> float:
+    qw, qx, qy, qz = quat
+    siny_cosp = 2.0 * (qw * qz + qx * qy)
+    cosy_cosp = 1.0 - 2.0 * (qy * qy + qz * qz)
+    return math.atan2(siny_cosp, cosy_cosp)
+
+
+def local_rect_polygon(
+    origin_x: float,
+    origin_y: float,
+    yaw: float,
+    center_x: float,
+    center_y: float,
+    length: float,
+    width: float,
+) -> list[tuple[float, float]]:
+    c = math.cos(yaw)
+    s = math.sin(yaw)
+    hx = length * 0.5
+    hy = width * 0.5
+    points = []
+    for lx, ly in [(-hx, -hy), (hx, -hy), (hx, hy), (-hx, hy)]:
+        px = center_x + lx
+        py = center_y + ly
+        points.append((origin_x + px * c - py * s, origin_y + px * s + py * c))
+    return points
+
+
+def robot_wheel_local_points() -> list[tuple[str, float, float]]:
+    wheels = []
+    for name, (pitch_x, pitch_y, knee_y, wheel_y, wheel_geom_y, knee_x) in ROBOT_WHEEL_POSITIONS.items():
+        x = ROBOT_BODY_CENTER_OFFSET_X + pitch_x + knee_x
+        y = pitch_y + knee_y + wheel_y + wheel_geom_y
+        wheels.append((name, x, y))
+    return wheels
+
+
+class DashboardButton:
+    def __init__(self, rect: tuple[int, int, int, int], label: str, action: str) -> None:
+        self.rect_tuple = rect
+        self.label = label
+        self.action = action
+
+    def rect(self, pygame: Any) -> Any:
+        return pygame.Rect(*self.rect_tuple)
+
+
+class PygameRouteDashboard:
+    def __init__(
+        self,
+        waypoints: list[Waypoint],
+        regions: list[AvoidRegion],
+        terrain_xml: Path,
+        width: int,
+        height: int,
+        fps: float,
+        max_trail: int,
+        control_hz: float,
+        initial_sim_speed: float,
+    ) -> None:
+        os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1")
+        # Older pkg_resources versions used by some pygame installs still
+        # reference pkgutil.ImpImporter, which was removed in Python 3.12.
+        if not hasattr(pkgutil, "ImpImporter"):
+            import zipimport
+            pkgutil.ImpImporter = zipimport.zipimporter  # type: ignore[attr-defined]
+        import pygame
+
+        self.pygame = pygame
+        pygame.init()
+        pygame.font.init()
+        self.screen = pygame.display.set_mode((width, height), pygame.RESIZABLE)
+        pygame.display.set_caption("nav_route_sim2sim_check dashboard")
+        self.clock = pygame.time.Clock()
+        self.font = pygame.font.Font(None, 24)
+        self.small_font = pygame.font.Font(None, 19)
+        self.title_font = pygame.font.Font(None, 28)
+        self.waypoints = waypoints
+        self.regions = regions
+        self.geoms = parse_dashboard_geoms(terrain_xml)
+        self.width = width
+        self.height = height
+        self.panel_width = 400
+        self.fps = max(1.0, float(fps))
+        self.control_hz = max(1.0, float(control_hz))
+        self.max_trail = max(50, int(max_trail))
+        self.trail: list[tuple[float, float]] = []
+        self.zoom = 60.0
+        self.pan_x = 0.0
+        self.pan_y = 0.0
+        self.speed_scale = 1.0
+        self.sim_speed = max(0.1, min(10.0, float(initial_sim_speed)))
+        self.rewind_seconds = 0.0
+        self.paused = False
+        self.buttons: list[DashboardButton] = []
+        self._dragging = False
+        self._drag_start: tuple[int, int] | None = None
+        self._last_draw_time = 0.0
+        self._fit_view()
+
+    @property
+    def map_width(self) -> int:
+        return max(240, self.width - self.panel_width)
+
+    def _fit_view(self) -> None:
+        points: list[tuple[float, float]] = [(wp.x, wp.y) for wp in self.waypoints]
+        for region in self.regions:
+            points.extend(region.polygon)
+        for geom in self.geoms:
+            if len(geom.pos) >= 2:
+                points.append((geom.pos[0], geom.pos[1]))
+        if not points:
+            return
+        xs = [p[0] for p in points]
+        ys = [p[1] for p in points]
+        span_x = max(1.0, max(xs) - min(xs))
+        span_y = max(1.0, max(ys) - min(ys))
+        margin = 70
+        self.zoom = max(
+            12.0,
+            min(
+                120.0,
+                min((self.map_width - margin * 2) / span_x, (self.height - margin * 2) / span_y),
+            ),
+        )
+        center_x = (min(xs) + max(xs)) * 0.5
+        center_y = (min(ys) + max(ys)) * 0.5
+        self.pan_x = -center_x * self.zoom
+        self.pan_y = center_y * self.zoom
+
+    def _screen(self, x: float, y: float) -> tuple[int, int]:
+        sx = int(self.map_width / 2.0 + x * self.zoom + self.pan_x)
+        sy = int(self.height / 2.0 - y * self.zoom + self.pan_y)
+        return sx, sy
+
+    def _draw_text(self, text: str, x: int, y: int, color: tuple[int, int, int] = (226, 232, 240), small: bool = False) -> None:
+        font = self.small_font if small else self.font
+        self.screen.blit(font.render(text, True, color), (x, y))
+
+    def _draw_card(self, rect: tuple[int, int, int, int], title: str | None = None) -> None:
+        pygame = self.pygame
+        pygame.draw.rect(self.screen, (20, 28, 48), rect, border_radius=7)
+        pygame.draw.rect(self.screen, (47, 61, 90), rect, width=1, border_radius=7)
+        if title:
+            self._draw_text(title, rect[0] + 12, rect[1] + 9, (203, 213, 225), small=True)
+
+    def _draw_bar(self, x: int, y: int, w: int, value: float, lo: float, hi: float, color: tuple[int, int, int]) -> None:
+        pygame = self.pygame
+        frac = 0.0 if hi <= lo else max(0.0, min(1.0, (value - lo) / (hi - lo)))
+        pygame.draw.rect(self.screen, (8, 13, 25), (x, y, w, 8), border_radius=4)
+        pygame.draw.rect(self.screen, color, (x, y, int(w * frac), 8), border_radius=4)
+
+    def _rgba_to_color(self, rgba: tuple[float, float, float, float], alpha: int | None = None) -> tuple[int, int, int] | tuple[int, int, int, int]:
+        rgb = tuple(max(0, min(255, int(v * 255))) for v in rgba[:3])
+        if alpha is None:
+            return rgb
+        return (*rgb, alpha)
+
+    def _draw_grid(self) -> None:
+        pygame = self.pygame
+        bg = (9, 14, 26)
+        grid = (28, 36, 58)
+        axis = (58, 72, 106)
+        self.screen.fill(bg)
+        step = 1.0
+        left_world = -(self.map_width / 2.0 + self.pan_x) / self.zoom
+        right_world = (self.map_width / 2.0 - self.pan_x) / self.zoom
+        bottom_world = -(self.height / 2.0 - self.pan_y) / self.zoom
+        top_world = (self.height / 2.0 + self.pan_y) / self.zoom
+        gx = math.floor(left_world / step) * step
+        while gx <= right_world:
+            sx, _ = self._screen(gx, 0.0)
+            pygame.draw.line(self.screen, axis if abs(gx) < 1e-6 else grid, (sx, 0), (sx, self.height), 2 if abs(gx) < 1e-6 else 1)
+            gx += step
+        gy = math.floor(bottom_world / step) * step
+        while gy <= top_world:
+            _, sy = self._screen(0.0, gy)
+            pygame.draw.line(self.screen, axis if abs(gy) < 1e-6 else grid, (0, sy), (self.map_width, sy), 2 if abs(gy) < 1e-6 else 1)
+            gy += step
+
+    def _draw_map(self, telemetry: dict[str, Any]) -> None:
+        pygame = self.pygame
+        self._draw_grid()
+        self._draw_xml_geoms()
+        overlay = pygame.Surface((self.map_width, self.height), pygame.SRCALPHA)
+        for region in self.regions:
+            pts = [self._screen(px, py) for px, py in region.polygon]
+            if len(pts) >= 3:
+                pygame.draw.polygon(overlay, (244, 63, 94, 72), pts)
+                pygame.draw.polygon(self.screen, (251, 113, 133), pts, width=2)
+        self.screen.blit(overlay, (0, 0))
+
+        route = [self._screen(wp.x, wp.y) for wp in self.waypoints]
+        if len(route) >= 2:
+            pygame.draw.lines(self.screen, (14, 165, 233), False, route, width=2)
+        active_index = int(telemetry.get("wp_index", 0))
+        for index, wp in enumerate(self.waypoints):
+            color = (148, 163, 184)
+            radius = 4
+            if index < active_index:
+                color = (34, 197, 94)
+            elif index == active_index:
+                color = (250, 204, 21)
+                radius = 7
+            pygame.draw.circle(self.screen, color, self._screen(wp.x, wp.y), radius)
+
+        if len(self.trail) >= 2:
+            pygame.draw.lines(self.screen, (34, 197, 94), False, [self._screen(x, y) for x, y in self.trail], width=3)
+
+        x = float(telemetry.get("x", 0.0))
+        y = float(telemetry.get("y", 0.0))
+        yaw = float(telemetry.get("yaw", 0.0))
+        self._draw_robot_pose(x, y, yaw)
+
+    def _draw_robot_pose(self, x: float, y: float, yaw: float) -> None:
+        pygame = self.pygame
+        body = local_rect_polygon(
+            x,
+            y,
+            yaw,
+            ROBOT_BODY_CENTER_OFFSET_X,
+            0.0,
+            ROBOT_BODY_LENGTH,
+            ROBOT_BODY_WIDTH,
+        )
+        body_screen = [self._screen(px, py) for px, py in body]
+        pygame.draw.polygon(self.screen, (30, 41, 59), body_screen)
+        pygame.draw.polygon(self.screen, (226, 232, 240), body_screen, width=2)
+
+        c = math.cos(yaw)
+        s = math.sin(yaw)
+        for _, lx, ly in robot_wheel_local_points():
+            wx = x + lx * c - ly * s
+            wy = y + lx * s + ly * c
+            wheel = local_rect_polygon(
+                wx,
+                wy,
+                yaw,
+                0.0,
+                0.0,
+                ROBOT_WHEEL_VIS_LENGTH,
+                ROBOT_WHEEL_VIS_WIDTH,
+            )
+            pygame.draw.polygon(self.screen, (2, 6, 23), [self._screen(px, py) for px, py in wheel])
+
+        origin = self._screen(x, y)
+        nose = self._screen(x + 0.32 * math.cos(yaw), y + 0.32 * math.sin(yaw))
+        radius = max(3, int(self.zoom * 0.025))
+        pygame.draw.circle(self.screen, (45, 212, 191), origin, radius)
+        pygame.draw.line(self.screen, (45, 212, 191), (origin[0] - radius - 2, origin[1]), (origin[0] + radius + 2, origin[1]), 1)
+        pygame.draw.line(self.screen, (45, 212, 191), (origin[0], origin[1] - radius - 2), (origin[0], origin[1] + radius + 2), 1)
+        pygame.draw.line(self.screen, (240, 253, 250), origin, nose, 2)
+
+    def _draw_xml_geoms(self) -> None:
+        pygame = self.pygame
+        geom_layer = pygame.Surface((self.map_width, self.height), pygame.SRCALPHA)
+        for geom in self.geoms:
+            if len(geom.pos) < 2 or not geom.size:
+                continue
+            color = self._rgba_to_color(geom.rgba, 130 if geom.collidable else 55)
+            outline = self._rgba_to_color(geom.rgba)
+            if geom.kind == "box" and len(geom.size) >= 2:
+                cx, cy = geom.pos[0], geom.pos[1]
+                sx, sy = geom.size[0], geom.size[1]
+                yaw = quat_yaw_wxyz(geom.quat)
+                points = []
+                for lx, ly in [(-sx, -sy), (sx, -sy), (sx, sy), (-sx, sy)]:
+                    wx = cx + lx * math.cos(yaw) - ly * math.sin(yaw)
+                    wy = cy + lx * math.sin(yaw) + ly * math.cos(yaw)
+                    points.append(self._screen(wx, wy))
+                pygame.draw.polygon(geom_layer, color, points)
+                pygame.draw.polygon(self.screen, outline, points, width=1)
+            elif geom.kind == "cylinder":
+                cx, cy = self._screen(geom.pos[0], geom.pos[1])
+                radius = max(2, int(abs(geom.size[0]) * self.zoom))
+                pygame.draw.circle(geom_layer, color, (cx, cy), radius)
+                pygame.draw.circle(self.screen, outline, (cx, cy), radius, width=1)
+            elif geom.kind == "plane":
+                continue
+        self.screen.blit(geom_layer, (0, 0))
+
+    def _draw_panel(self, telemetry: dict[str, Any]) -> None:
+        pygame = self.pygame
+        x0 = self.map_width
+        pygame.draw.rect(self.screen, (12, 18, 32), (x0, 0, self.panel_width, self.height))
+        pygame.draw.line(self.screen, (55, 65, 88), (x0, 0), (x0, self.height), 2)
+        x = x0 + 16
+        y = 14
+        self.screen.blit(self.title_font.render("Route Monitor", True, (248, 250, 252)), (x, y))
+        self._draw_text("MuJoCo sim2sim telemetry", x, y + 25, (148, 163, 184), small=True)
+
+        y += 58
+        card_w = self.panel_width - 32
+        self._draw_card((x, y, card_w, 112), "state")
+        mode_color = (250, 204, 21) if str(telemetry.get("mode", "")).lower().startswith("crawl") else (45, 212, 191)
+        self._draw_text(str(telemetry.get("mode", "rough")), x + 12, y + 31, mode_color)
+        self._draw_text(f"t {float(telemetry.get('time', 0.0)):.2f}s", x + 130, y + 31, (226, 232, 240))
+        self._draw_text(
+            f"wp {telemetry.get('wp_id', '')}  {int(telemetry.get('wp_index', 0))}/{int(telemetry.get('wp_count', 0))}",
+            x + 12,
+            y + 60,
+            (226, 232, 240),
+        )
+        self._draw_text(
+            f"x {float(telemetry.get('x', 0.0)):.2f}  y {float(telemetry.get('y', 0.0)):.2f}  yaw {math.degrees(float(telemetry.get('yaw', 0.0))):.1f}",
+            x + 12,
+            y + 86,
+            (148, 163, 184),
+            small=True,
+        )
+
+        y += 124
+        self._draw_card((x, y, card_w, 128), "command limits")
+        cmd_vx = float(telemetry.get("cmd_vx", 0.0))
+        cmd_vy = float(telemetry.get("cmd_vy", 0.0))
+        cmd_wz = float(telemetry.get("cmd_wz", 0.0))
+        for row, (name, value, color) in enumerate(
+            [
+                ("x", cmd_vx, (34, 197, 94)),
+                ("y", cmd_vy, (14, 165, 233)),
+                ("yaw", cmd_wz, (250, 204, 21)),
+            ]
+        ):
+            yy = y + 32 + row * 30
+            self._draw_text(f"{name} {value:+.3f}", x + 12, yy - 7, (226, 232, 240), small=True)
+            self._draw_bar(x + 90, yy, 245, value, -1.0, 1.0, color)
+            cx = x + 90 + 245 // 2
+            pygame.draw.line(self.screen, (71, 85, 105), (cx, yy - 3), (cx, yy + 11), 1)
+
+        y += 140
+        self._draw_card((x, y, card_w, 112), "safety")
+        margin = float(telemetry.get("margin", 0.0))
+        margin_color = (251, 113, 133) if margin < 0.0 else (34, 197, 94)
+        self._draw_text(f"margin {margin:+.3f} m", x + 12, y + 32, margin_color)
+        self._draw_text(f"clearance {float(telemetry.get('clearance', 0.0)):.3f} m", x + 190, y + 32, (226, 232, 240))
+        self._draw_text(f"region {telemetry.get('region', '')}", x + 12, y + 62, (148, 163, 184), small=True)
+        self._draw_text(
+            f"roll {float(telemetry.get('roll_deg', 0.0)):.1f}  pitch {float(telemetry.get('pitch_deg', 0.0)):.1f}  tilt {float(telemetry.get('max_tilt_deg', 0.0)):.1f}",
+            x + 12,
+            y + 84,
+            (148, 163, 184),
+            small=True,
+        )
+
+        y += 124
+        self._draw_controls(x, y, card_w)
+
+    def _draw_controls(self, x: int, y: int, card_w: int) -> None:
+        pygame = self.pygame
+        self._draw_card((x, y, card_w, min(245, self.height - y - 12)), "controls")
+        x += 12
+        y += 32
+        self._draw_text(f"nav {self.speed_scale:.2f}x", x, y, (226, 232, 240))
+        self._draw_text(f"sim {self.sim_speed:.1f}x", x + 132, y, (226, 232, 240))
+        self._draw_text(f"rtf {float(self._last_rtf):.1f}x" if hasattr(self, "_last_rtf") else "rtf --", x + 250, y, (148, 163, 184), small=True)
+        y += 30
+        labels = [
+            ("nav -", "speed_down"),
+            ("nav +", "speed_up"),
+            ("nav 1x", "speed_normal"),
+            ("sim -", "sim_down"),
+            ("sim +", "sim_up"),
+            ("sim 1x", "sim_normal"),
+            ("sim 5x", "sim_5"),
+            ("sim 10x", "sim_10"),
+            ("rew -1s", "rewind_1"),
+            ("rew -5s", "rewind_5"),
+            ("pause", "pause"),
+            ("fit", "fit"),
+        ]
+        self.buttons = []
+        bx = x
+        by = y
+        for i, (label, action) in enumerate(labels):
+            if i and i % 3 == 0:
+                bx = x
+                by += 36
+            button = DashboardButton((bx, by, 104, 28), label, action)
+            rect = button.rect(pygame)
+            fill = (31, 41, 55)
+            if action == "pause" and self.paused:
+                fill = (245, 158, 11)
+            elif action == "speed_normal" and abs(self.speed_scale - 1.0) < 1e-6:
+                fill = (16, 185, 129)
+            elif action == "sim_normal" and abs(self.sim_speed - 1.0) < 1e-6:
+                fill = (14, 165, 233)
+            pygame.draw.rect(self.screen, fill, rect, border_radius=5)
+            pygame.draw.rect(self.screen, (71, 85, 105), rect, width=1, border_radius=5)
+            text = self.small_font.render(label, True, (248, 250, 252))
+            self.screen.blit(text, (rect.centerx - text.get_width() // 2, rect.centery - text.get_height() // 2))
+            self.buttons.append(button)
+            bx += 112
+        by += 48
+        self._draw_text("keys: +/- nav, [/ ] sim, backspace rewind, space pause", x, by, (148, 163, 184), small=True)
+        self._draw_text("wheel zoom, right drag pan, f fit", x, by + 20, (148, 163, 184), small=True)
+
+    def pump_events(self) -> bool:
+        pygame = self.pygame
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                return False
+            if event.type == pygame.KEYDOWN:
+                if event.key in (pygame.K_EQUALS, pygame.K_PLUS, pygame.K_KP_PLUS):
+                    self.speed_scale = min(10.0, self.speed_scale + 0.10)
+                elif event.key in (pygame.K_MINUS, pygame.K_KP_MINUS):
+                    self.speed_scale = max(0.05, self.speed_scale - 0.10)
+                elif event.key == pygame.K_0:
+                    self.speed_scale = 1.0
+                elif event.key == pygame.K_RIGHTBRACKET:
+                    self.sim_speed = self._next_sim_speed(1)
+                elif event.key == pygame.K_LEFTBRACKET:
+                    self.sim_speed = self._next_sim_speed(-1)
+                elif event.key == pygame.K_BACKSPACE:
+                    self.rewind_seconds = max(self.rewind_seconds, 1.0)
+                elif event.key == pygame.K_SPACE:
+                    self.paused = not self.paused
+                elif event.key == pygame.K_f:
+                    self._fit_view()
+            if event.type == pygame.VIDEORESIZE:
+                self.width = max(700, int(event.w))
+                self.height = max(480, int(event.h))
+                self.screen = pygame.display.set_mode((self.width, self.height), pygame.RESIZABLE)
+                self._fit_view()
+            if event.type == pygame.MOUSEBUTTONDOWN:
+                if event.button == 1:
+                    for button in self.buttons:
+                        if button.rect(pygame).collidepoint(event.pos):
+                            self._handle_button(button.action)
+                            break
+                elif event.button == 3:
+                    self._dragging = True
+                    self._drag_start = event.pos
+                elif event.button == 4 and event.pos[0] < self.map_width:
+                    self.zoom = min(240.0, self.zoom * 1.12)
+                elif event.button == 5 and event.pos[0] < self.map_width:
+                    self.zoom = max(8.0, self.zoom / 1.12)
+            if event.type == pygame.MOUSEBUTTONUP and event.button == 3:
+                self._dragging = False
+                self._drag_start = None
+            if event.type == pygame.MOUSEMOTION and self._dragging and self._drag_start is not None:
+                dx = event.pos[0] - self._drag_start[0]
+                dy = event.pos[1] - self._drag_start[1]
+                self.pan_x += dx
+                self.pan_y += dy
+                self._drag_start = event.pos
+        return True
+
+    def draw_if_due(self, telemetry: dict[str, Any], force: bool = False) -> None:
+        now = time.perf_counter()
+        draw_interval = 1.0 / max(1.0, self.fps)
+        if force or now - self._last_draw_time >= draw_interval:
+            self._last_rtf = float(telemetry.get("rtf", 0.0))
+            self.trail.append((float(telemetry.get("x", 0.0)), float(telemetry.get("y", 0.0))))
+            if len(self.trail) > self.max_trail:
+                self.trail = self.trail[-self.max_trail :]
+            self._draw_map(telemetry)
+            self._draw_panel(telemetry)
+            self.pygame.display.flip()
+            self._last_draw_time = now
+
+    def _handle_button(self, action: str) -> None:
+        if action == "speed_down":
+            self.speed_scale = max(0.05, self.speed_scale - 0.10)
+        elif action == "speed_up":
+            self.speed_scale = min(10.0, self.speed_scale + 0.10)
+        elif action == "speed_normal":
+            self.speed_scale = 1.0
+        elif action == "sim_down":
+            self.sim_speed = self._next_sim_speed(-1)
+        elif action == "sim_up":
+            self.sim_speed = self._next_sim_speed(1)
+        elif action == "sim_normal":
+            self.sim_speed = 1.0
+        elif action == "sim_5":
+            self.sim_speed = 5.0
+        elif action == "sim_10":
+            self.sim_speed = 10.0
+        elif action == "rewind_1":
+            self.rewind_seconds = max(self.rewind_seconds, 1.0)
+        elif action == "rewind_5":
+            self.rewind_seconds = max(self.rewind_seconds, 5.0)
+        elif action == "pause":
+            self.paused = not self.paused
+        elif action == "fit":
+            self._fit_view()
+
+    def _next_sim_speed(self, direction: int) -> float:
+        steps = [0.2, 0.5, 0.8, 1.0, 1.5, 2.0, 3.0, 5.0, 10.0]
+        current = float(self.sim_speed)
+        if direction > 0:
+            for value in steps:
+                if value > current + 1e-6:
+                    return value
+            return steps[-1]
+        for value in reversed(steps):
+            if value < current - 1e-6:
+                return value
+        return steps[0]
+
+    def consume_rewind_seconds(self) -> float:
+        value = self.rewind_seconds
+        self.rewind_seconds = 0.0
+        return value
+
+    def close(self) -> None:
+        self.pygame.quit()
+
+
+def capture_route_snapshot(io: MuJoCoIO, follower: WaypointFollower) -> RouteSnapshot:
+    return RouteSnapshot(
+        time=float(io.d.time),
+        qpos=io.d.qpos.copy(),
+        qvel=io.d.qvel.copy(),
+        ctrl=io.d.ctrl.copy(),
+        follower_index=int(follower.index),
+        follower_turn_in_place=bool(follower.turn_in_place),
+        follower_stable_count=int(follower.stable_count),
+        follower_wp_start_time=float(follower.wp_start_time),
+        follower_best_dist=float(getattr(follower, "best_dist", 0.0)) if hasattr(follower, "best_dist") else None,
+        follower_last_progress_time=float(getattr(follower, "last_progress_time", 0.0)) if hasattr(follower, "last_progress_time") else None,
+        follower_recovery_until=float(getattr(follower, "recovery_until", 0.0)) if hasattr(follower, "recovery_until") else None,
+        follower_recovery_count=int(getattr(follower, "recovery_count", 0)) if hasattr(follower, "recovery_count") else None,
+        follower_recovery_turn_sign=float(getattr(follower, "recovery_turn_sign", 1.0)) if hasattr(follower, "recovery_turn_sign") else None,
+    )
+
+
+def restore_route_snapshot(io: MuJoCoIO, follower: WaypointFollower, snapshot: RouteSnapshot) -> None:
+    io.d.time = snapshot.time
+    io.d.qpos[:] = snapshot.qpos
+    io.d.qvel[:] = snapshot.qvel
+    io.d.ctrl[:] = snapshot.ctrl
+    follower.index = snapshot.follower_index
+    follower.turn_in_place = snapshot.follower_turn_in_place
+    follower.stable_count = snapshot.follower_stable_count
+    follower.wp_start_time = snapshot.follower_wp_start_time
+    if snapshot.follower_best_dist is not None and hasattr(follower, "best_dist"):
+        setattr(follower, "best_dist", snapshot.follower_best_dist)
+    if snapshot.follower_last_progress_time is not None and hasattr(follower, "last_progress_time"):
+        setattr(follower, "last_progress_time", snapshot.follower_last_progress_time)
+    if snapshot.follower_recovery_until is not None and hasattr(follower, "recovery_until"):
+        setattr(follower, "recovery_until", snapshot.follower_recovery_until)
+    if snapshot.follower_recovery_count is not None and hasattr(follower, "recovery_count"):
+        setattr(follower, "recovery_count", snapshot.follower_recovery_count)
+    if snapshot.follower_recovery_turn_sign is not None and hasattr(follower, "recovery_turn_sign"):
+        setattr(follower, "recovery_turn_sign", snapshot.follower_recovery_turn_sign)
+    mujoco.mj_forward(io.m, io.d)
+
+
+def run_sim(args: argparse.Namespace) -> tuple[SimResult, Path | None, Path]:
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+    stamp = time.strftime("%Y%m%d_%H%M%S") + f"_{time.time_ns() % 1_000_000_000:09d}"
+    run_dir = args.out_dir / f"route_check_{stamp}"
+    run_dir.mkdir(parents=True, exist_ok=True)
+
+    waypoints, regions = load_requested_route(args)
+    footprint_radius = (
+        float(args.footprint_radius)
+        if args.footprint_radius is not None
+        else default_lateral_footprint_radius()
+    )
+    required_clearance = footprint_radius + float(args.avoid_margin)
+    cfg = SimConfig(
+        max_vx=float(args.max_vx),
+        max_vy=float(args.max_vy),
+        max_wz=float(args.max_wz),
+        kp_dist=float(args.kp_dist),
+        kp_yaw=float(args.kp_yaw),
+        yaw_stop_threshold_deg=float(args.yaw_stop_threshold_deg),
+        turn_in_place_enter_deg=float(args.turn_in_place_enter_deg),
+        turn_in_place_exit_deg=float(args.turn_in_place_exit_deg),
+        max_total_time_s=float(args.max_time),
+        waypoint_timeout_s=float(args.waypoint_timeout),
+        lookahead_m=float(args.lookahead),
+        min_cmd_vx=float(args.min_cmd_vx),
+        creep_cmd_vx=float(args.creep_cmd_vx),
+        cmd_vx_scale=float(args.cmd_vx_scale),
+        stuck_timeout_s=float(args.stuck_timeout),
+        recovery_duration_s=float(args.recovery_duration),
+        max_recoveries=int(args.max_recoveries),
+        slalom_script_enabled=bool(args.slalom_script),
+        slalom_script_start_tolerance=float(args.slalom_script_start_tolerance),
+        slalom_script_pos_tolerance=float(args.slalom_script_pos_tolerance),
+        slalom_script_yaw_tolerance_deg=float(args.slalom_script_yaw_tolerance_deg),
+        slalom_script_stable_cycles=int(args.slalom_script_stable_cycles),
+        slalom_script_rotate_steps_enabled=bool(args.slalom_script_rotate_steps),
+        slalom_script_final_rotate_enabled=bool(args.slalom_script_final_rotate),
+        slalom_script_require_yaw_at_step=bool(args.slalom_script_require_yaw_at_step),
+        slalom_script_kp_dist=float(args.slalom_script_kp_dist),
+        slalom_script_kp_yaw=float(args.slalom_script_kp_yaw),
+        slalom_script_max_vx=float(args.slalom_script_max_vx),
+        slalom_script_max_vy=float(args.slalom_script_max_vy),
+        slalom_script_max_wz=float(args.slalom_script_max_wz),
+        slalom_script_min_cmd_linear=float(args.slalom_script_min_cmd_linear),
+        slalom_script_min_cmd_angular=float(args.slalom_script_min_cmd_angular),
+        slalom_script_min_cmd_epsilon=float(args.slalom_script_min_cmd_epsilon),
+        slalom_script_min_step_distance=float(args.slalom_script_min_step_distance),
+        slalom_script_yaw_gate_deg=float(args.slalom_script_yaw_gate_deg),
+        slalom_script_drive_yaw_source=str(args.slalom_script_drive_yaw_source),
+        slalom_script_curvature_speed_enabled=bool(args.slalom_script_curvature_speed),
+        slalom_script_curvature_min_scale=max(
+            0.1, min(1.0, float(args.slalom_script_curvature_min_scale))
+        ),
+    )
+
+    combined_xml = make_combined_terrain_xml(args.terrain_xml.resolve(), run_dir)
+    old_cwd = Path.cwd()
+    try:
+        io = MuJoCoIO(combined_xml, args.robot_xml.resolve(), args.hfield_dir.resolve())
+    finally:
+        os.chdir(old_cwd)
+    if args.policy_backend == "rough-ik-crawl":
+        policy = RoughPolicyIkCrawlBackend(args.onnx.resolve())
+    elif args.policy_backend == "policy-runner":
+        crawl_path = args.crawl_onnx.resolve() if args.crawl_onnx and args.crawl_onnx.exists() else None
+        policy = ExistingPolicyBackend(args.onnx.resolve(), crawl_path)
+    else:
+        policy = DirectOnnxBackend(args.onnx.resolve(), DEPLOY_DEFAULT_DOF_POS)
+    follower: WaypointFollower
+    if args.follower in {"slalom-script", "nav-script"}:
+        follower = SlalomScriptFollower(waypoints, cfg)
+    elif args.follower == "nav-good":
+        follower = NavGoodFollower(waypoints, cfg)
+    elif args.follower == "nav-sim2sim":
+        follower = NavSim2SimFollower(waypoints, cfg)
+    elif args.follower == "pure-pursuit":
+        follower = PurePursuitFollower(waypoints, cfg)
+    else:
+        follower = WaypointFollower(waypoints, cfg)
+    if (
+        args.skip_initial_waypoint
+        or (args.auto_skip_slice_start and int(args.start_index) > 1)
+    ) and len(waypoints) > 1:
+        follower.index = 1
+
+    start_x, start_y, start_yaw_deg = initial_pose_for_route(args, waypoints)
+    start_x += float(args.start_x_offset)
+    start_y += float(args.start_y_offset)
+    set_robot_pose(
+        io,
+        start_x,
+        start_y,
+        start_yaw_deg,
+        float(args.start_z),
+        int(args.settle_steps),
+        policy.default_dof_pos,
+    )
+    policy.reset()
+    follower.reset_timing(io.d.time)
+
+    sim_steps_per_control = max(1, int(round((1.0 / cfg.control_hz) / io.m.opt.timestep)))
+    csv_path = None if args.no_csv else run_dir / "trajectory.csv"
+    csv_file = None
+    writer = None
+    if csv_path is not None:
+        csv_file = csv_path.open("w", newline="", encoding="utf-8")
+        writer = csv.writer(csv_file)
+        writer.writerow(
+            [
+                "time",
+                "wp_index",
+                "wp_id",
+                "script_active",
+                "script_step",
+                "script_step_count",
+                "x",
+                "y",
+                "z",
+                "yaw_deg",
+                "roll_deg",
+                "pitch_deg",
+                "cmd_vx",
+                "cmd_vy",
+                "cmd_wz",
+                "clearance",
+                "margin",
+                "region",
+                "raw_wheel_fl",
+                "raw_wheel_fr",
+                "raw_wheel_rl",
+                "raw_wheel_rr",
+                "qvel_wheel_fl",
+                "qvel_wheel_fr",
+                "qvel_wheel_rl",
+                "qvel_wheel_rr",
+            ]
+        )
+
+    min_clearance = float("inf")
+    min_clearance_region = ""
+    min_clearance_wp = ""
+    max_roll = 0.0
+    max_pitch = 0.0
+    max_tilt = 0.0
+    samples = 0
+    reason = "max time reached"
+    success = False
+
+    viewer = None
+    if args.viewer:
+        viewer = mujoco.viewer.launch_passive(io.m, io.d)
+        viewer.cam.distance = 5.0
+        viewer.cam.elevation = -35.0
+        viewer.cam.azimuth = 135.0
+    sim_speed_factor = max(0.1, min(10.0, float(args.sim_speed)))
+    if args.dashboard:
+        sim_speed_factor = max(0.1, min(10.0, float(args.dashboard_sim_speed)))
+    dashboard = None
+    if args.dashboard:
+        dashboard = PygameRouteDashboard(
+            waypoints=waypoints,
+            regions=regions,
+            terrain_xml=args.terrain_xml.resolve(),
+            width=int(args.dashboard_width),
+            height=int(args.dashboard_height),
+            fps=float(args.dashboard_fps),
+            max_trail=int(args.dashboard_trail),
+            control_hz=cfg.control_hz,
+            initial_sim_speed=sim_speed_factor,
+        )
+    snapshots: list[RouteSnapshot] = []
+    snapshot_keep_s = 45.0
+    control_dt = 1.0 / cfg.control_hz
+    next_exec_time = time.perf_counter()
+    wall_start_time = next_exec_time
+    sim_start_time = float(io.d.time)
+    randomizer = random.Random(int(args.random_seed))
+    command_delay_steps = max(0, int(round(float(args.command_delay_ms) * 0.001 * cfg.control_hz)))
+    action_delay_steps = max(0, int(round(float(args.action_delay_ms) * 0.001 * cfg.control_hz)))
+    command_queue: deque[np.ndarray] = deque()
+    action_queue: deque[np.ndarray] = deque()
+    last_deployed_command = np.zeros(3, dtype=np.float32)
+    base_body_id = mujoco.mj_name2id(io.m, mujoco.mjtObj.mjOBJ_BODY, "base_link")
+
+    try:
+        while float(io.d.time) - sim_start_time < cfg.max_total_time_s:
+            if viewer is not None and not viewer.is_running():
+                reason = "viewer closed"
+                break
+            if dashboard is not None and not dashboard.pump_events():
+                reason = "dashboard closed"
+                break
+            if dashboard is not None:
+                sim_speed_factor = max(0.1, min(10.0, float(dashboard.sim_speed)))
+            x = float(io.d.qpos[0])
+            y = float(io.d.qpos[1])
+            z = float(io.d.qpos[2])
+            yaw = robot_yaw_from_qpos(io.d.qpos)
+            sensed_x = x
+            sensed_y = y
+            sensed_yaw = yaw
+            if args.deployment_randomization:
+                sensed_x += randomizer.gauss(0.0, max(0.0, float(args.pose_noise_xy)))
+                sensed_y += randomizer.gauss(0.0, max(0.0, float(args.pose_noise_xy)))
+                sensed_yaw += math.radians(
+                    randomizer.gauss(0.0, max(0.0, float(args.pose_noise_yaw_deg)))
+                )
+            heading_yaw = follower.normalize_angle(sensed_yaw + math.radians(float(args.heading_offset_deg)))
+            roll, pitch, _ = quat_to_euler_wxyz(io.d.qpos[3:7].copy())
+            roll_deg = math.degrees(roll)
+            pitch_deg = math.degrees(pitch)
+            max_roll = max(max_roll, abs(roll_deg))
+            max_pitch = max(max_pitch, abs(pitch_deg))
+            max_tilt = max(max_tilt, math.hypot(roll_deg, pitch_deg))
+
+            active = follower.active
+            active_id = active.id if active is not None else "done"
+            clearance, region_name = clearance_to_regions(x, y, regions)
+            margin = clearance - required_clearance
+            if clearance < min_clearance:
+                min_clearance = clearance
+                min_clearance_region = region_name
+                min_clearance_wp = active_id
+
+            if z < float(args.min_base_z) or abs(roll_deg) > 70.0 or abs(pitch_deg) > 70.0:
+                reason = f"robot fell or tipped at t={io.d.time:.2f}s"
+                break
+            if args.stop_on_clearance_violation and margin < 0.0:
+                reason = (
+                    f"clearance violation at t={io.d.time:.2f}s: "
+                    f"clearance={clearance:.3f} required={required_clearance:.3f} region={region_name}"
+                )
+                break
+
+            command, nav_state = follower.update(sensed_x, sensed_y, heading_yaw, io.d.time)
+            if nav_state == "complete":
+                success = True
+                reason = "route complete"
+                break
+            if nav_state and nav_state.startswith("timeout"):
+                reason = nav_state
+                break
+            if nav_state:
+                reason = nav_state
+                break
+            skip_local_safety = False
+            if active is not None and (active.policy or "").lower() == "crawl":
+                skip_local_safety = True
+            if hasattr(follower, "_is_charge_segment") and active is not None:
+                skip_local_safety = bool(getattr(follower, "_is_charge_segment")(x, y, active)) or skip_local_safety
+            if args.local_safety and not skip_local_safety:
+                command = safety_filter_command(
+                    command,
+                    x,
+                    y,
+                    heading_yaw,
+                    follower.active,
+                    regions,
+                    required_clearance,
+                    cfg,
+                    float(args.safety_horizon),
+                    float(args.safety_dt),
+                )
+            if dashboard is not None:
+                command[0] = float(command[0]) * dashboard.speed_scale
+                if dashboard.paused:
+                    command[:] = 0.0
+            command = np.clip(command, -1.0, 1.0).astype(np.float32)
+            if args.deployment_randomization:
+                if randomizer.random() < max(0.0, min(1.0, float(args.command_dropout_prob))):
+                    command = last_deployed_command.copy()
+                command_queue.append(command.copy())
+                command = command_queue.popleft() if len(command_queue) > command_delay_steps else np.zeros(3, dtype=np.float32)
+                last_deployed_command = command.copy()
+
+            use_route_policy = args.enable_policy_switch or args.policy_backend == "rough-ik-crawl"
+            active_policy = active.policy if active is not None and use_route_policy else "rough"
+            policy.maybe_switch_policy(active_policy)
+            policy_mode = str(getattr(policy, "requested_mode", getattr(getattr(policy, "runner", None), "current_policy_name", active_policy)))
+            default_dof_pos = policy.default_dof_pos
+            obs = io.get_obs_53d(command, default_dof_pos, policy.last_actions)
+            scaled_actions, raw_actions = policy.step(obs)
+            if args.deployment_randomization:
+                action_queue.append(scaled_actions.copy())
+                scaled_actions = action_queue.popleft() if len(action_queue) > action_delay_steps else np.zeros_like(scaled_actions)
+            io.send_actions(scaled_actions, default_dof_pos)
+            for _ in range(sim_steps_per_control):
+                if args.deployment_randomization and base_body_id != -1:
+                    io.d.xfrc_applied[base_body_id] = 0.0
+                    push_phase = (float(io.d.time) - sim_start_time) % max(0.1, float(args.push_interval))
+                    if push_phase < max(0.0, float(args.push_duration)):
+                        angle = randomizer.uniform(-math.pi, math.pi)
+                        force = max(0.0, float(args.push_force))
+                        io.d.xfrc_applied[base_body_id, 0] = force * math.cos(angle)
+                        io.d.xfrc_applied[base_body_id, 1] = force * math.sin(angle)
+                mujoco.mj_step(io.m, io.d)
+                pole_contact = None
+                pole_contact_robot_geom = None
+                pole_contact_robot_body = None
+                pole_contact_position = None
+                border_contact = None
+                for contact_index in range(io.d.ncon):
+                    contact = io.d.contact[contact_index]
+                    geom1 = mujoco.mj_id2name(io.m, mujoco.mjtObj.mjOBJ_GEOM, contact.geom1) or ""
+                    geom2 = mujoco.mj_id2name(io.m, mujoco.mjtObj.mjOBJ_GEOM, contact.geom2) or ""
+                    if geom1.startswith("slalom_pole_") or geom2.startswith("slalom_pole_"):
+                        pole_contact = geom1 if geom1.startswith("slalom_pole_") else geom2
+                        robot_geom_id = contact.geom2 if geom1.startswith("slalom_pole_") else contact.geom1
+                        pole_contact_robot_geom = geom2 if geom1.startswith("slalom_pole_") else geom1
+                        robot_body_id = int(io.m.geom_bodyid[robot_geom_id])
+                        pole_contact_robot_body = (
+                            mujoco.mj_id2name(io.m, mujoco.mjtObj.mjOBJ_BODY, robot_body_id) or "unknown"
+                        )
+                        pole_contact_position = tuple(float(value) for value in contact.pos)
+                        break
+                    if geom1.startswith("border_") or geom2.startswith("border_"):
+                        border_contact = geom1 if geom1.startswith("border_") else geom2
+                        break
+                if pole_contact is not None:
+                    contact_xyz = ""
+                    if pole_contact_position is not None:
+                        contact_xyz = (
+                            f" at xyz=({pole_contact_position[0]:.3f},"
+                            f"{pole_contact_position[1]:.3f},{pole_contact_position[2]:.3f})"
+                        )
+                    reason = (
+                        f"physical contact: robot body {pole_contact_robot_body or 'unknown'} "
+                        f"geom {pole_contact_robot_geom or 'unnamed'} with "
+                        f"{pole_contact}{contact_xyz} at t={io.d.time:.2f}s near waypoint {active_id}"
+                    )
+                    break
+                if border_contact is not None:
+                    reason = f"physical contact with field boundary {border_contact} at t={io.d.time:.2f}s near waypoint {active_id}"
+                    break
+            if pole_contact is not None or border_contact is not None:
+                break
+            if dashboard is not None:
+                snapshots.append(capture_route_snapshot(io, follower))
+                cutoff_time = float(io.d.time) - snapshot_keep_s
+                while len(snapshots) > 2 and snapshots[0].time < cutoff_time:
+                    snapshots.pop(0)
+            viewer_sync_stride = max(1, int(args.viewer_sync_every), int(math.ceil(max(1.0, sim_speed_factor) * 2.0)))
+            if viewer is not None and samples % viewer_sync_stride == 0:
+                base_id = mujoco.mj_name2id(io.m, mujoco.mjtObj.mjOBJ_BODY, "base_link")
+                if base_id != -1:
+                    viewer.cam.lookat[:] = io.d.xpos[base_id]
+                viewer.sync()
+
+            if dashboard is not None:
+                wall_elapsed = max(1e-6, time.perf_counter() - wall_start_time)
+                rtf = max(0.0, (float(io.d.time) - sim_start_time) / wall_elapsed)
+                telemetry = {
+                    "time": float(io.d.time),
+                    "rtf": rtf,
+                    "status": "running",
+                    "mode": policy_mode,
+                    "wp_index": follower.index,
+                    "wp_count": len(waypoints),
+                    "wp_id": active_id,
+                    "x": x,
+                    "y": y,
+                    "z": z,
+                    "yaw": yaw,
+                    "roll_deg": roll_deg,
+                    "pitch_deg": pitch_deg,
+                    "cmd_vx": float(command[0]),
+                    "cmd_vy": float(command[1]),
+                    "cmd_wz": float(command[2]),
+                    "clearance": clearance,
+                    "margin": margin,
+                    "region": region_name,
+                    "min_margin": min_clearance - required_clearance,
+                    "max_tilt_deg": max_tilt,
+                }
+                dashboard.draw_if_due(telemetry)
+                rewind_seconds = dashboard.consume_rewind_seconds()
+                if rewind_seconds > 0.0 and snapshots:
+                    target_time = max(0.0, float(io.d.time) - rewind_seconds)
+                    rewind_snapshot = snapshots[0]
+                    for snapshot in snapshots:
+                        if snapshot.time <= target_time:
+                            rewind_snapshot = snapshot
+                        else:
+                            break
+                    restore_route_snapshot(io, follower, rewind_snapshot)
+                    snapshots = [snapshot for snapshot in snapshots if snapshot.time <= rewind_snapshot.time]
+                    if hasattr(policy, "reset"):
+                        policy.reset()
+                    reason = "max time reached"
+                    next_exec_time = time.perf_counter()
+
+            if writer is not None and samples % max(1, int(args.sample_every)) == 0:
+                writer.writerow(
+                    [
+                        f"{io.d.time:.4f}",
+                        follower.index,
+                        active_id,
+                        int(bool(getattr(follower, "slalom_script_active", False))),
+                        int(getattr(follower, "slalom_script_step_index", -1)),
+                        len(getattr(follower, "slalom_script_steps", [])),
+                        f"{x:.5f}",
+                        f"{y:.5f}",
+                        f"{z:.5f}",
+                        f"{math.degrees(yaw):.3f}",
+                        f"{roll_deg:.3f}",
+                        f"{pitch_deg:.3f}",
+                        f"{float(command[0]):.4f}",
+                        f"{float(command[1]):.4f}",
+                        f"{float(command[2]):.4f}",
+                        f"{clearance:.5f}",
+                        f"{margin:.5f}",
+                        region_name,
+                        f"{raw_actions[12]:.5f}",
+                        f"{raw_actions[13]:.5f}",
+                        f"{raw_actions[14]:.5f}",
+                        f"{raw_actions[15]:.5f}",
+                        f"{io.d.qvel[io.qvel_ids[12]]:.5f}",
+                        f"{io.d.qvel[io.qvel_ids[13]]:.5f}",
+                        f"{io.d.qvel[io.qvel_ids[14]]:.5f}",
+                        f"{io.d.qvel[io.qvel_ids[15]]:.5f}",
+                    ]
+                )
+            samples += 1
+            jitter_s = 0.0
+            if args.deployment_randomization:
+                jitter_s = randomizer.uniform(
+                    -max(0.0, float(args.control_jitter_ms)) * 0.001,
+                    max(0.0, float(args.control_jitter_ms)) * 0.001,
+                )
+            dt_real = max(0.001, control_dt + jitter_s) / sim_speed_factor
+            next_exec_time += dt_real
+            now = time.perf_counter()
+            sleep_time = next_exec_time - now
+            if sleep_time > 0.0:
+                time.sleep(sleep_time)
+            elif sleep_time < -dt_real:
+                next_exec_time = now
+    finally:
+        wall_time = max(1e-6, time.perf_counter() - wall_start_time)
+        elapsed_sim_time = max(0.0, float(io.d.time) - sim_start_time)
+        real_time_factor = max(0.0, elapsed_sim_time / wall_time)
+        if csv_file is not None:
+            csv_file.close()
+        if viewer is not None:
+            viewer.close()
+        if dashboard is not None:
+            dashboard.close()
+        policy.close()
+
+    result = SimResult(
+        success=success,
+        reason=reason,
+        sim_time=elapsed_sim_time,
+        reached_count=follower.index,
+        waypoint_count=len(waypoints),
+        min_clearance=float(min_clearance),
+        min_margin=float(min_clearance - required_clearance),
+        min_clearance_region=min_clearance_region,
+        min_clearance_wp=min_clearance_wp,
+        max_roll_deg=max_roll,
+        max_pitch_deg=max_pitch,
+        max_tilt_deg=max_tilt,
+        samples=samples,
+        wall_time=wall_time,
+        real_time_factor=real_time_factor,
+    )
+    report = {
+        "success": result.success,
+        "reason": result.reason,
+        "sim_time": round(result.sim_time, 4),
+        "reached_count": result.reached_count,
+        "waypoint_count": result.waypoint_count,
+        "min_clearance": round(result.min_clearance, 6),
+        "required_clearance": round(required_clearance, 6),
+        "min_margin": round(result.min_margin, 6),
+        "min_clearance_region": result.min_clearance_region,
+        "min_clearance_wp": result.min_clearance_wp,
+        "max_roll_deg": round(result.max_roll_deg, 3),
+        "max_pitch_deg": round(result.max_pitch_deg, 3),
+        "max_tilt_deg": round(result.max_tilt_deg, 3),
+        "samples": result.samples,
+        "wall_time": round(result.wall_time, 4),
+        "real_time_factor": round(result.real_time_factor, 4),
+        "sim_speed": round(sim_speed_factor, 4),
+        "points": str(args.points.resolve()),
+        "mission": args.mission,
+        "terrain_xml": str(args.terrain_xml.resolve()),
+        "onnx": str(args.onnx.resolve()),
+        "crawl_onnx": str(args.crawl_onnx.resolve()) if args.crawl_onnx else None,
+        "policy_backend": args.policy_backend,
+        "enable_policy_switch": args.enable_policy_switch,
+        "follower": args.follower,
+        "max_vx": args.max_vx,
+        "max_vy": args.max_vy,
+        "max_wz": args.max_wz,
+        "lookahead": args.lookahead,
+        "cmd_vx_scale": args.cmd_vx_scale,
+        "slalom_script": args.slalom_script,
+        "slalom_script_start_tolerance": args.slalom_script_start_tolerance,
+        "slalom_script_pos_tolerance": args.slalom_script_pos_tolerance,
+        "slalom_script_kp_dist": args.slalom_script_kp_dist,
+        "slalom_script_kp_yaw": args.slalom_script_kp_yaw,
+        "slalom_script_max_vx": args.slalom_script_max_vx,
+        "slalom_script_max_vy": args.slalom_script_max_vy,
+        "slalom_script_max_wz": args.slalom_script_max_wz,
+        "slalom_script_min_cmd_linear": args.slalom_script_min_cmd_linear,
+        "slalom_script_min_cmd_angular": args.slalom_script_min_cmd_angular,
+        "slalom_script_min_cmd_epsilon": args.slalom_script_min_cmd_epsilon,
+        "slalom_script_yaw_gate_deg": args.slalom_script_yaw_gate_deg,
+        "slalom_script_drive_yaw_source": args.slalom_script_drive_yaw_source,
+        "local_safety": args.local_safety,
+        "safety_horizon": args.safety_horizon,
+        "start_yaw_offset_deg": args.start_yaw_offset_deg,
+        "start_x_offset": args.start_x_offset,
+        "start_y_offset": args.start_y_offset,
+        "heading_offset_deg": args.heading_offset_deg,
+        "start_z": args.start_z,
+        "settle_steps": args.settle_steps,
+        "viewer": args.viewer,
+        "start_index": args.start_index,
+        "end_index": args.end_index,
+        "skip_initial_waypoint": args.skip_initial_waypoint,
+        "auto_skip_slice_start": args.auto_skip_slice_start,
+        "trajectory_csv": str(csv_path) if csv_path else None,
+    }
+    (run_dir / "report.json").write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+    return result, csv_path, run_dir
+
+
+def main() -> int:
+    args = parse_args()
+    result, csv_path, run_dir = run_sim(args)
+    print("Route sim2sim check")
+    print(f"  success: {result.success}")
+    print(f"  reason: {result.reason}")
+    print(f"  reached: {result.reached_count}/{result.waypoint_count}")
+    print(f"  sim_time: {result.sim_time:.2f}s")
+    print(f"  wall_time: {result.wall_time:.2f}s rtf={result.real_time_factor:.2f}x")
+    print(
+        "  clearance: "
+        f"min={result.min_clearance:.3f}m margin={result.min_margin:.3f}m "
+        f"region={result.min_clearance_region} wp={result.min_clearance_wp}"
+    )
+    print(
+        "  attitude: "
+        f"max_roll={result.max_roll_deg:.1f}deg "
+        f"max_pitch={result.max_pitch_deg:.1f}deg max_tilt={result.max_tilt_deg:.1f}deg"
+    )
+    print(f"  report_dir: {run_dir}")
+    if csv_path:
+        print(f"  trajectory: {csv_path}")
+    return 0 if result.success and result.min_margin >= 0.0 else 2
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/05_software/train/rc_mjlab/sim2sim/nav_sim2sim.py b/05_software/train/rc_mjlab/sim2sim/nav_sim2sim.py
index 9e1a740..b798dac 100644
--- a/05_software/train/rc_mjlab/sim2sim/nav_sim2sim.py
+++ b/05_software/train/rc_mjlab/sim2sim/nav_sim2sim.py
@@ -324,9 +324,11 @@ def main():
     terrain_dir = Path(__file__).parent / "terrain"
     terrain_xml = terrain_dir / "scene_terrain.xml"
     robot_xml = project_root / "mjcf" / "wheelleg.xml"
+    rough_onnx = project_root / "model_6800.onnx"
+    crawl_onnx = project_root / "model_crawl.onnx"
     policy_path = {
-        "rough": project_root / "model_rough.pt",
-        "crawl": project_root / "model_crawl.pt"
+        "rough": rough_onnx if rough_onnx.exists() else project_root / "model_rough.pt",
+        "crawl": crawl_onnx if crawl_onnx.exists() else project_root / "model_crawl.pt"
     }
 
     # 1. 解析 XML 地图障碍物，实现 100% 可视化精准对应
diff --git a/05_software/train/rc_mjlab/sim2sim/policy/policy_runner.py b/05_software/train/rc_mjlab/sim2sim/policy/policy_runner.py
index 3b15db4..b5a3134 100644
--- a/05_software/train/rc_mjlab/sim2sim/policy/policy_runner.py
+++ b/05_software/train/rc_mjlab/sim2sim/policy/policy_runner.py
@@ -3,7 +3,10 @@ import torch.nn as nn
 import numpy as np
 from collections import deque
 from pathlib import Path
-from pynput import keyboard
+try:
+    from pynput import keyboard
+except ImportError:
+    keyboard = None
 
 # ============================================================
 # Policy Model
@@ -26,6 +29,28 @@ class PolicyMLP(nn.Module):
 
 
 def load_policy(model_path, device):
+    if str(model_path).endswith('.onnx'):
+        import onnxruntime as ort
+        session = ort.InferenceSession(str(model_path))
+        class OnnxWrapper:
+            def __init__(self, session):
+                self.session = session
+                self.obs_dim = session.get_inputs()[0].shape[1]
+                if isinstance(self.obs_dim, str):
+                    self.obs_dim = 53
+                class MockMean:
+                    def __init__(self, d):
+                        self.d = d
+                    def numel(self):
+                        return self.d
+                self.obs_mean = MockMean(self.obs_dim)
+
+            def __call__(self, x):
+                inputs = {self.session.get_inputs()[0].name: x.cpu().numpy()}
+                out = self.session.run(None, inputs)[0]
+                return torch.tensor(out, device=x.device)
+        return OnnxWrapper(session)
+
     ckpt = torch.load(model_path, map_location=device, weights_only=False)
     state_dict = ckpt["actor_state_dict"]
     
@@ -129,9 +154,13 @@ class PolicyRunner:
         ], dtype=np.float32)
 
         # Background keyboard listener for seamless switcher keys ('1' and '2')
-        self.listener = keyboard.Listener(on_press=self._on_press)
-        self.listener.start()
-        print("[PolicyRunner] Background Keyboard Switcher active: Press '1' for ROUGH, '2' for CRAWL")
+        self.listener = None
+        if keyboard is not None:
+            self.listener = keyboard.Listener(on_press=self._on_press)
+            self.listener.start()
+            print("[PolicyRunner] Background Keyboard Switcher active: Press '1' for ROUGH, '2' for CRAWL")
+        else:
+            print("[PolicyRunner] pynput not installed; background keyboard switcher disabled.")
 
     def _on_press(self, key):
         try:
diff --git a/05_software/train/rc_mjlab/sim2sim/requirements.txt b/05_software/train/rc_mjlab/sim2sim/requirements.txt
new file mode 100644
index 0000000..4da9002
--- /dev/null
+++ b/05_software/train/rc_mjlab/sim2sim/requirements.txt
@@ -0,0 +1,3 @@
+# Additional runtime dependencies for the post-training Sim2Sim tools.
+onnxruntime>=1.19.0
+pygame>=2.6.1
diff --git a/05_software/train/rc_mjlab/sim2sim/sim2sim.py b/05_software/train/rc_mjlab/sim2sim/sim2sim.py
index 91560c8..c6adf7c 100644
--- a/05_software/train/rc_mjlab/sim2sim/sim2sim.py
+++ b/05_software/train/rc_mjlab/sim2sim/sim2sim.py
@@ -41,6 +41,19 @@ class PolicyMLP(nn.Module):
 
 
 def load_policy(model_path, device):
+    if str(model_path).endswith('.onnx'):
+        import onnxruntime as ort
+        session = ort.InferenceSession(str(model_path))
+        class OnnxWrapper:
+            def __init__(self, session):
+                self.session = session
+            def __call__(self, x):
+                inputs = {self.session.get_inputs()[0].name: x.cpu().numpy()}
+                out = self.session.run(None, inputs)[0]
+                return torch.tensor(out, device=x.device)
+        return OnnxWrapper(session)
+
+
     ckpt = torch.load(model_path, map_location=device, weights_only=False)
     state_dict = ckpt["actor_state_dict"]
     model = PolicyMLP()
@@ -131,7 +144,9 @@ def main():
     terrain_dir = Path(__file__).parent / "terrain"
     terrain_xml = terrain_dir / "scene_terrain.xml"
     robot_xml = Path(__file__).parent.parent / "mjcf" / "wheelleg.xml"
-    policy_path = Path(__file__).parent.parent / "model_1700.pt"
+    policy_path = Path(__file__).parent.parent / "model_6800.onnx"
+    if not policy_path.exists():
+        policy_path = Path(__file__).parent.parent / "model_rough.pt"
     hfield_dir = terrain_dir
 
     temp_xml = project_root / "mjcf" / "sim2sim_temp.xml"
diff --git a/05_software/train/rc_mjlab/tools/nav_tools/README.md b/05_software/train/rc_mjlab/tools/nav_tools/README.md
new file mode 100644
index 0000000..2b672c1
--- /dev/null
+++ b/05_software/train/rc_mjlab/tools/nav_tools/README.md
@@ -0,0 +1,7 @@
+# 路线检查公共模块
+
+`route_safety_check.py` 提供航点、避障区域、机器人平面包络和几何距离计算，供 `sim2sim/nav_route_sim2sim_check.py` 复用。
+
+该模块只依赖 Python 标准库。原开发目录中的地图编辑器、PCD、比赛路线 JSON、备份和批量实验结果不属于本次后期 Sim2Sim 里程碑，未在这里复制。
+
+外部路线文件需要包含 `waypoints`（或 `segments[].waypoints`）以及 `regions` / `avoid_regions`。也可以不提供路线文件，直接使用 Sim2Sim 检查器的内置任务。
diff --git a/05_software/train/rc_mjlab/tools/nav_tools/route_safety_check.py b/05_software/train/rc_mjlab/tools/nav_tools/route_safety_check.py
new file mode 100644
index 0000000..1e3dca3
--- /dev/null
+++ b/05_software/train/rc_mjlab/tools/nav_tools/route_safety_check.py
@@ -0,0 +1,638 @@
+#!/usr/bin/env python3
+"""Offline route safety checker for nav_tools waypoint JSON files.
+
+The checker treats avoid regions as hard no-go polygons and validates the
+route centerline with a circular robot footprint. It is intentionally light on
+dependencies so it can run on the robot laptop without ROS, pygame, or shapely.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+import xml.etree.ElementTree as ET
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+
+ROBOT_BODY_LENGTH = 0.356
+ROBOT_BODY_WIDTH = 0.235
+ROBOT_BODY_CENTER_X = 0.1518
+ROBOT_ORIGIN_FROM_FRONT = 0.105
+ROBOT_WHEEL_VIS_LENGTH = 0.16
+ROBOT_WHEEL_VIS_WIDTH = 0.055
+ROBOT_POSE_HIP = 0.550
+ROBOT_POSE_KNEE = -1.125
+PCD_ROBOT_RADIUS = 0.18
+ROBOT_FOOTPRINT_PADDING = 0.03
+
+
+@dataclass(frozen=True)
+class Waypoint:
+    index: int
+    id: str
+    x: float
+    y: float
+    yaw_deg: float
+    speed: float | None
+    policy: str
+    tolerance: float | None
+    slalom_straight: bool = False
+    slalom_script_break: bool = False
+    slalom_script_pos_tolerance: float | None = None
+    exact_reach: bool = False
+    precision_follow: bool = False
+    require_yaw: bool = False
+    yaw_tolerance_deg: float | None = None
+    stable_cycles: int | None = None
+    mandatory_cross: bool = False
+    mandatory_radius: float | None = None
+    mandatory_center_x: float | None = None
+    mandatory_center_y: float | None = None
+
+
+@dataclass(frozen=True)
+class AvoidRegion:
+    name: str
+    kind: str
+    polygon: tuple[tuple[float, float], ...]
+
+
+@dataclass(frozen=True)
+class SegmentRisk:
+    start_id: str
+    end_id: str
+    region: str
+    clearance_m: float
+    required_m: float
+    margin_m: float
+    length_m: float
+    centerline_intersects: bool
+
+    @property
+    def status(self) -> str:
+        if self.centerline_intersects:
+            return "INTERSECT"
+        if self.margin_m < 0.0:
+            return "VIOLATION"
+        if self.margin_m < 0.05:
+            return "TIGHT"
+        return "OK"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Check nav_tools waypoint routes against avoid/no-go polygons."
+    )
+    parser.add_argument(
+        "--points",
+        type=Path,
+        default=Path("tools/nav_tools/points/points_20260705_174627.json"),
+        help="Route JSON exported by nav_map_viewer.",
+    )
+    parser.add_argument(
+        "--xml",
+        type=Path,
+        default=Path("tools/nav_tools/xml/A.xml"),
+        help="Optional MuJoCo terrain XML used for metadata checks.",
+    )
+    parser.add_argument(
+        "--onnx",
+        type=Path,
+        default=Path("model_6800.onnx"),
+        help="Optional ONNX policy path used for input/output shape reporting.",
+    )
+    parser.add_argument(
+        "--footprint-radius",
+        type=float,
+        default=None,
+        help="Robot circular footprint radius in meters. Defaults to sim2real lateral footprint.",
+    )
+    parser.add_argument(
+        "--avoid-margin",
+        type=float,
+        default=0.05,
+        help="Extra clearance added outside the robot footprint.",
+    )
+    parser.add_argument(
+        "--warn-margin",
+        type=float,
+        default=0.05,
+        help="Report a TIGHT warning when spare margin is below this value.",
+    )
+    parser.add_argument(
+        "--top",
+        type=int,
+        default=20,
+        help="Number of closest segment-region pairs to print.",
+    )
+    parser.add_argument(
+        "--json-out",
+        type=Path,
+        default=None,
+        help="Optional machine-readable report path.",
+    )
+    parser.add_argument(
+        "--allow-violations",
+        action="store_true",
+        help="Exit with code 0 even when violations are detected.",
+    )
+    return parser.parse_args()
+
+
+def load_json(path: Path) -> dict[str, Any]:
+    with path.open("r", encoding="utf-8") as f:
+        data = json.load(f)
+    if not isinstance(data, dict):
+        raise ValueError(f"JSON root must be an object: {path}")
+    return data
+
+
+def _optional_float(value: Any) -> float | None:
+    if value is None:
+        return None
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def load_waypoints(payload: dict[str, Any]) -> list[Waypoint]:
+    rows = None
+    if isinstance(payload.get("segments"), list) and payload["segments"]:
+        rows = []
+        for segment in payload["segments"]:
+            if isinstance(segment, dict) and isinstance(segment.get("waypoints"), list):
+                rows.extend(segment["waypoints"])
+    if rows is None:
+        rows = payload.get("waypoints")
+    if not isinstance(rows, list):
+        raise ValueError("Route JSON has no top-level waypoints or segments[].waypoints")
+
+    waypoints: list[Waypoint] = []
+    for index, row in enumerate(rows, start=1):
+        if not isinstance(row, dict):
+            continue
+        x = float(row.get("world_x", row.get("x", 0.0)))
+        y = float(row.get("world_y", row.get("y", 0.0)))
+        yaw = float(row.get("yawDeg", row.get("yaw_deg", row.get("yaw", 0.0))))
+        speed = row.get("speed")
+        tolerance = row.get("tolerance")
+        waypoints.append(
+            Waypoint(
+                index=index,
+                id=str(row.get("id", index)),
+                x=x,
+                y=y,
+                yaw_deg=yaw,
+                speed=float(speed) if speed is not None else None,
+                policy=str(row.get("policy", "")),
+                tolerance=float(tolerance) if tolerance is not None else None,
+                slalom_straight=bool(row.get("slalom_straight", row.get("slalomStraight", False))),
+                slalom_script_break=bool(
+                    row.get("slalom_script_break", row.get("slalomScriptBreak", False))
+                ),
+                slalom_script_pos_tolerance=_optional_float(
+                    row.get(
+                        "slalom_script_pos_tolerance",
+                        row.get("slalomScriptPosTolerance", row.get("scriptTolerance")),
+                    )
+                ),
+                exact_reach=bool(row.get("exact_reach", row.get("exactReach", False))),
+                precision_follow=bool(row.get("precision_follow", row.get("precisionFollow", False))),
+                require_yaw=bool(row.get("require_yaw", row.get("requireYaw", False))),
+                yaw_tolerance_deg=_optional_float(
+                    row.get("yaw_tolerance_deg", row.get("yawToleranceDeg"))
+                ),
+                stable_cycles=(
+                    int(row.get("stable_cycles", row.get("stableCycles")))
+                    if row.get("stable_cycles", row.get("stableCycles")) is not None
+                    else None
+                ),
+                mandatory_cross=bool(row.get("mandatory_cross", row.get("mandatoryCross", False))),
+                mandatory_radius=_optional_float(
+                    row.get("mandatory_radius", row.get("mandatoryRadius"))
+                ),
+                mandatory_center_x=_optional_float(
+                    row.get("mandatory_center_x", row.get("mandatoryCenterX"))
+                ),
+                mandatory_center_y=_optional_float(
+                    row.get("mandatory_center_y", row.get("mandatoryCenterY"))
+                ),
+            )
+        )
+    if len(waypoints) < 2:
+        raise ValueError("Route must contain at least two waypoints")
+    return waypoints
+
+
+def load_regions(payload: dict[str, Any]) -> list[AvoidRegion]:
+    rows = payload.get("regions", payload.get("avoid_regions", []))
+    if not isinstance(rows, list):
+        return []
+
+    regions: list[AvoidRegion] = []
+    for index, row in enumerate(rows, start=1):
+        if not isinstance(row, dict):
+            continue
+        polygon_rows = row.get("polygon", row.get("points", []))
+        if not isinstance(polygon_rows, list):
+            continue
+        polygon: list[tuple[float, float]] = []
+        for point in polygon_rows:
+            if isinstance(point, dict):
+                polygon.append((float(point.get("x", 0.0)), float(point.get("y", 0.0))))
+            elif isinstance(point, (list, tuple)) and len(point) >= 2:
+                polygon.append((float(point[0]), float(point[1])))
+        if len(polygon) >= 3:
+            regions.append(
+                AvoidRegion(
+                    name=str(row.get("name", f"avoid_{index}")),
+                    kind=str(row.get("kind", "avoid")),
+                    polygon=tuple(polygon),
+                )
+            )
+    return regions
+
+
+def robot_wheel_local_points(body_center_offset_x: float) -> list[tuple[float, float]]:
+    thigh_dx = -0.25 * math.sin(ROBOT_POSE_HIP)
+    shank_dx = -0.2 * math.sin(ROBOT_POSE_HIP + ROBOT_POSE_KNEE)
+    wheel_positions = (
+        ((0.32826 + 0.06389) - ROBOT_BODY_CENTER_X, 0.066172 - 0.027344, 0.1035, 0.014699, 0.04074, 0.0),
+        ((0.32826 + 0.06389) - ROBOT_BODY_CENTER_X, -0.065853 + 0.027311, -0.1035, -0.018447, -0.040735, -0.00075079),
+        ((-0.024743 - 0.06389) - ROBOT_BODY_CENTER_X, 0.066141 - 0.027309, 0.099459, 0.012475, 0.040737, 0.0),
+        ((-0.024743 - 0.06389) - ROBOT_BODY_CENTER_X, -0.065884 + 0.027341, -0.099408, -0.012435, -0.040737, -0.00075079),
+    )
+    return [
+        (
+            body_center_offset_x + pitch_x + knee_x + thigh_dx + shank_dx,
+            pitch_y + knee_y + wheel_y + wheel_geom_y,
+        )
+        for pitch_x, pitch_y, knee_y, wheel_y, wheel_geom_y, knee_x in wheel_positions
+    ]
+
+
+def default_lateral_footprint_radius() -> float:
+    half_width = ROBOT_BODY_WIDTH * 0.5
+    radius = max(PCD_ROBOT_RADIUS, half_width)
+    body_center_offset_x = ROBOT_ORIGIN_FROM_FRONT - ROBOT_BODY_LENGTH * 0.5
+    for _, wheel_y in robot_wheel_local_points(body_center_offset_x):
+        radius = max(radius, abs(wheel_y) + ROBOT_WHEEL_VIS_WIDTH * 0.5)
+    return radius + ROBOT_FOOTPRINT_PADDING
+
+
+def point_segment_distance(
+    px: float,
+    py: float,
+    ax: float,
+    ay: float,
+    bx: float,
+    by: float,
+) -> float:
+    dx = bx - ax
+    dy = by - ay
+    length_sq = dx * dx + dy * dy
+    if length_sq <= 1.0e-12:
+        return math.hypot(px - ax, py - ay)
+    t = ((px - ax) * dx + (py - ay) * dy) / length_sq
+    t = max(0.0, min(1.0, t))
+    qx = ax + t * dx
+    qy = ay + t * dy
+    return math.hypot(px - qx, py - qy)
+
+
+def orientation(
+    ax: float,
+    ay: float,
+    bx: float,
+    by: float,
+    cx: float,
+    cy: float,
+) -> float:
+    return (bx - ax) * (cy - ay) - (by - ay) * (cx - ax)
+
+
+def on_segment(
+    ax: float,
+    ay: float,
+    bx: float,
+    by: float,
+    cx: float,
+    cy: float,
+) -> bool:
+    return (
+        min(ax, bx) - 1.0e-9 <= cx <= max(ax, bx) + 1.0e-9
+        and min(ay, by) - 1.0e-9 <= cy <= max(ay, by) + 1.0e-9
+        and abs(orientation(ax, ay, bx, by, cx, cy)) <= 1.0e-9
+    )
+
+
+def segments_intersect(
+    a: tuple[float, float],
+    b: tuple[float, float],
+    c: tuple[float, float],
+    d: tuple[float, float],
+) -> bool:
+    ax, ay = a
+    bx, by = b
+    cx, cy = c
+    dx, dy = d
+    o1 = orientation(ax, ay, bx, by, cx, cy)
+    o2 = orientation(ax, ay, bx, by, dx, dy)
+    o3 = orientation(cx, cy, dx, dy, ax, ay)
+    o4 = orientation(cx, cy, dx, dy, bx, by)
+    if o1 * o2 < 0.0 and o3 * o4 < 0.0:
+        return True
+    return (
+        on_segment(ax, ay, bx, by, cx, cy)
+        or on_segment(ax, ay, bx, by, dx, dy)
+        or on_segment(cx, cy, dx, dy, ax, ay)
+        or on_segment(cx, cy, dx, dy, bx, by)
+    )
+
+
+def point_in_polygon(x: float, y: float, polygon: tuple[tuple[float, float], ...]) -> bool:
+    inside = False
+    for index, (ax, ay) in enumerate(polygon):
+        bx, by = polygon[(index + 1) % len(polygon)]
+        if point_segment_distance(x, y, ax, ay, bx, by) <= 1.0e-9:
+            return True
+        if (ay > y) != (by > y):
+            x_cross = (bx - ax) * (y - ay) / (by - ay) + ax
+            if x < x_cross:
+                inside = not inside
+    return inside
+
+
+def segment_polygon_intersects(
+    a: tuple[float, float],
+    b: tuple[float, float],
+    polygon: tuple[tuple[float, float], ...],
+) -> bool:
+    if point_in_polygon(a[0], a[1], polygon) or point_in_polygon(b[0], b[1], polygon):
+        return True
+    return any(
+        segments_intersect(a, b, polygon[index], polygon[(index + 1) % len(polygon)])
+        for index in range(len(polygon))
+    )
+
+
+def segment_polygon_distance(
+    a: tuple[float, float],
+    b: tuple[float, float],
+    polygon: tuple[tuple[float, float], ...],
+) -> float:
+    if segment_polygon_intersects(a, b, polygon):
+        return 0.0
+    distances = [point_segment_distance(px, py, a[0], a[1], b[0], b[1]) for px, py in polygon]
+    for index, (ax, ay) in enumerate(polygon):
+        bx, by = polygon[(index + 1) % len(polygon)]
+        distances.append(point_segment_distance(a[0], a[1], ax, ay, bx, by))
+        distances.append(point_segment_distance(b[0], b[1], ax, ay, bx, by))
+    return min(distances)
+
+
+def analyze_route(
+    waypoints: list[Waypoint],
+    regions: list[AvoidRegion],
+    required_clearance: float,
+) -> list[SegmentRisk]:
+    risks: list[SegmentRisk] = []
+    for start, end in zip(waypoints, waypoints[1:]):
+        a = (start.x, start.y)
+        b = (end.x, end.y)
+        length = math.hypot(end.x - start.x, end.y - start.y)
+        for region in regions:
+            intersects = segment_polygon_intersects(a, b, region.polygon)
+            clearance = 0.0 if intersects else segment_polygon_distance(a, b, region.polygon)
+            risks.append(
+                SegmentRisk(
+                    start_id=start.id,
+                    end_id=end.id,
+                    region=region.name,
+                    clearance_m=clearance,
+                    required_m=required_clearance,
+                    margin_m=clearance - required_clearance,
+                    length_m=length,
+                    centerline_intersects=intersects,
+                )
+            )
+    risks.sort(key=lambda item: (item.margin_m, item.clearance_m))
+    return risks
+
+
+def parse_xml_summary(path: Path) -> dict[str, Any]:
+    if not path.exists():
+        return {"path": str(path), "exists": False}
+    root = ET.parse(path).getroot()
+    geoms = [geom for geom in root.iter("geom")]
+    collidable = [
+        geom for geom in geoms
+        if geom.get("name") != "floor"
+        and geom.get("contype", "1") != "0"
+        and geom.get("conaffinity", "1") != "0"
+    ]
+    return {
+        "path": str(path),
+        "exists": True,
+        "model": root.get("model", ""),
+        "geom_count": len(geoms),
+        "collidable_geom_count": len(collidable),
+    }
+
+
+def parse_onnx_summary(path: Path) -> dict[str, Any]:
+    if not path.exists():
+        return {"path": str(path), "exists": False}
+    try:
+        import onnx  # type: ignore
+    except Exception as exc:  # pragma: no cover - depends on local env
+        return {"path": str(path), "exists": True, "error": f"onnx import failed: {exc}"}
+
+    model = onnx.load(str(path))
+    inputs = [
+        {
+            "name": item.name,
+            "shape": [
+                dim.dim_value if dim.dim_value else dim.dim_param
+                for dim in item.type.tensor_type.shape.dim
+            ],
+        }
+        for item in model.graph.input
+    ]
+    outputs = [
+        {
+            "name": item.name,
+            "shape": [
+                dim.dim_value if dim.dim_value else dim.dim_param
+                for dim in item.type.tensor_type.shape.dim
+            ],
+        }
+        for item in model.graph.output
+    ]
+    return {
+        "path": str(path),
+        "exists": True,
+        "inputs": inputs,
+        "outputs": outputs,
+        "metadata_keys": [prop.key for prop in model.metadata_props],
+    }
+
+
+def risk_to_dict(risk: SegmentRisk) -> dict[str, Any]:
+    return {
+        "start_id": risk.start_id,
+        "end_id": risk.end_id,
+        "region": risk.region,
+        "clearance_m": round(risk.clearance_m, 6),
+        "required_m": round(risk.required_m, 6),
+        "margin_m": round(risk.margin_m, 6),
+        "length_m": round(risk.length_m, 6),
+        "centerline_intersects": risk.centerline_intersects,
+        "status": risk.status,
+    }
+
+
+def print_report(
+    points_path: Path,
+    xml_summary: dict[str, Any],
+    onnx_summary: dict[str, Any],
+    waypoints: list[Waypoint],
+    regions: list[AvoidRegion],
+    footprint_radius: float,
+    avoid_margin: float,
+    warn_margin: float,
+    risks: list[SegmentRisk],
+    top: int,
+) -> None:
+    required_clearance = footprint_radius + avoid_margin
+    violations = [risk for risk in risks if risk.margin_m < 0.0 or risk.centerline_intersects]
+    tight = [
+        risk for risk in risks
+        if risk.margin_m >= 0.0 and risk.margin_m < warn_margin
+    ]
+    route_len = sum(
+        math.hypot(b.x - a.x, b.y - a.y)
+        for a, b in zip(waypoints, waypoints[1:])
+    )
+
+    print("Route safety check")
+    print(f"  points: {points_path}")
+    print(f"  waypoints: {len(waypoints)}, regions: {len(regions)}, path_length: {route_len:.3f} m")
+    print(
+        "  clearance: "
+        f"footprint={footprint_radius:.3f} m + avoid_margin={avoid_margin:.3f} m "
+        f"=> required={required_clearance:.3f} m"
+    )
+    if xml_summary.get("exists"):
+        print(
+            "  xml: "
+            f"{xml_summary.get('path')} "
+            f"(model={xml_summary.get('model')}, geoms={xml_summary.get('geom_count')}, "
+            f"collidable={xml_summary.get('collidable_geom_count')})"
+        )
+    else:
+        print(f"  xml: missing ({xml_summary.get('path')})")
+    if onnx_summary.get("exists") and not onnx_summary.get("error"):
+        print(f"  onnx: {onnx_summary.get('path')}")
+        print(f"    inputs: {onnx_summary.get('inputs')}")
+        print(f"    outputs: {onnx_summary.get('outputs')}")
+    elif onnx_summary.get("exists"):
+        print(f"  onnx: {onnx_summary.get('error')}")
+    else:
+        print(f"  onnx: missing ({onnx_summary.get('path')})")
+
+    print("")
+    if violations:
+        print(f"FAIL: {len(violations)} segment-region pairs are inside required clearance.")
+    elif tight:
+        print(f"WARN: no violations, but {len(tight)} segment-region pairs are tight.")
+    else:
+        print("PASS: all segment-region pairs satisfy the requested clearance.")
+
+    print("")
+    print(f"Closest {min(top, len(risks))} segment-region pairs:")
+    print("  status      wp_start->wp_end  region       clear   req     spare")
+    for risk in risks[:top]:
+        print(
+            f"  {risk.status:<10}  "
+            f"{risk.start_id:>4}->{risk.end_id:<4}      "
+            f"{risk.region:<10}  "
+            f"{risk.clearance_m:>6.3f}  "
+            f"{risk.required_m:>6.3f}  "
+            f"{risk.margin_m:>7.3f}"
+        )
+
+
+def main() -> int:
+    args = parse_args()
+    points_path = args.points.resolve()
+    xml_path = args.xml.resolve()
+    onnx_path = args.onnx.resolve()
+
+    payload = load_json(points_path)
+    waypoints = load_waypoints(payload)
+    regions = load_regions(payload)
+    if not regions:
+        raise ValueError(f"No avoid regions found in {points_path}")
+
+    footprint_radius = (
+        float(args.footprint_radius)
+        if args.footprint_radius is not None
+        else default_lateral_footprint_radius()
+    )
+    required_clearance = footprint_radius + float(args.avoid_margin)
+    risks = analyze_route(waypoints, regions, required_clearance)
+    xml_summary = parse_xml_summary(xml_path)
+    onnx_summary = parse_onnx_summary(onnx_path)
+
+    print_report(
+        points_path,
+        xml_summary,
+        onnx_summary,
+        waypoints,
+        regions,
+        footprint_radius,
+        float(args.avoid_margin),
+        float(args.warn_margin),
+        risks,
+        max(0, int(args.top)),
+    )
+
+    violations = [risk for risk in risks if risk.margin_m < 0.0 or risk.centerline_intersects]
+    tight = [
+        risk for risk in risks
+        if risk.margin_m >= 0.0 and risk.margin_m < float(args.warn_margin)
+    ]
+    report = {
+        "points": str(points_path),
+        "waypoint_count": len(waypoints),
+        "region_count": len(regions),
+        "footprint_radius_m": round(footprint_radius, 6),
+        "avoid_margin_m": round(float(args.avoid_margin), 6),
+        "required_clearance_m": round(required_clearance, 6),
+        "violations": [risk_to_dict(risk) for risk in violations],
+        "tight": [risk_to_dict(risk) for risk in tight],
+        "closest": [risk_to_dict(risk) for risk in risks[: max(0, int(args.top))]],
+        "xml": xml_summary,
+        "onnx": onnx_summary,
+    }
+    if args.json_out:
+        args.json_out.parent.mkdir(parents=True, exist_ok=True)
+        args.json_out.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+
+    if violations and not args.allow_violations:
+        return 2
+    return 0
+
+
+if __name__ == "__main__":
+    try:
+        raise SystemExit(main())
+    except Exception as exc:
+        print(f"ERROR: {exc}", file=sys.stderr)
+        raise SystemExit(1)
diff --git a/README.md b/README.md
index e6b0c26..dd76f47 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 RC_WheelLeg 是山东华宇工学院 16DOF 串联轮足机器人项目。
 
-当前 `16dof` 分支用于整理 16DOF 机械、强化学习训练、Sim2Sim、Sim2Real、ROS 2 部署和比赛版本。机械资料、比赛训练架构和后期 MuJoCo 工具集已经完成整理。
+当前 `16dof` 分支用于整理 16DOF 机械、强化学习训练、Sim2Sim、Sim2Real、ROS 2 部署和比赛版本。机械资料、比赛训练架构、后期 MuJoCo 工具集和后期 Sim2Sim 已经完成整理。
 
 ## 平台概览
 
@@ -37,6 +37,7 @@ RC_WheelLeg/
 - [x] 整理第二版 Sim2Real 随机化训练配置
 - [x] 整理比赛最终训练代码架构
 - [x] 整理后期 MuJoCo 姿态、IK、动力学和 MPC 工具
+- [x] 整理后期 Sim2Sim、路线检查与比赛 Rough ONNX 策略
 - [ ] 核对比赛机械与仿真模型参数
 - [ ] 整理 URDF/MJCF 机器人描述
 - [ ] 整理后续统一训练、ROS 2 和比赛版本