[software] 添加16DOF早期训练仿真与Sim2Real闭环

2026-07-21 16:15:14 +08:00
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+Welcome to mjlab!
+=================
+
+.. figure:: source/_static/mjlab-banner.jpg
+   :width: 100%
+   :alt: mjlab
+
+mjlab is a lightweight, open-source framework for robot learning that
+combines GPU-accelerated simulation with composable environments and minimal
+setup friction. It adopts the manager-based API introduced by
+`Isaac Lab <https://github.com/isaac-sim/IsaacLab>`_, where users compose
+modular building blocks for observations, rewards, and events, and pairs it
+with `MuJoCo Warp <https://github.com/google-deepmind/mujoco_warp>`_ for
+GPU-accelerated physics. The result is a framework installable with a single
+command, requiring minimal dependencies, and providing direct access to
+native `MuJoCo <https://github.com/google-deepmind/mujoco>`_ data
+structures.
+
+**Key features:**
+
+- **Composable environments:** users define observations, rewards,
+  terminations, and other MDP terms as modular building blocks
+- **Minimal dependencies:** single-command install via ``uv``, low startup
+  latency
+- **Direct MuJoCo data structures:** native ``MjModel``/``MjData`` access
+  with no translation layers
+- **PyTorch-native:** observations, rewards, and actions are PyTorch
+  tensors backed by zero-copy GPU memory sharing
+
+For more on the design decisions behind mjlab, see :doc:`source/motivation`.
+
+**Try it now** (no installation needed):
+
+.. code-block:: bash
+
+   uvx --from mjlab --refresh demo
+
+Table of Contents
+-----------------
+
+.. toctree::
+   :maxdepth: 1
+   :caption: User Guide
+
+   source/installation
+   source/tutorials
+   source/contributing
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Concepts
+
+   source/architecture_overview
+   source/entity/index
+   source/actuators
+   source/sensors/index
+   source/scene
+   source/terrain
+
+.. toctree::
+   :maxdepth: 1
+   :caption: The Manager Layer
+
+   source/environment_config
+   source/observations
+   source/actions
+   source/rewards
+   source/terminations
+   source/commands
+   source/events
+   source/randomization
+   source/curriculum
+   source/metrics
+   source/recorders
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Training & Debugging
+
+   source/training/rsl_rl
+   source/viewers
+   source/training/distributed_training
+   source/training/cloud
+   source/debugging/nan_guard
+   source/debugging/export_scene
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API Reference
+
+   source/api/index
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Further Reading
+
+   source/motivation
+   source/migration_isaac_lab
+   source/faq
+   source/research
+   source/changelog
+
+License & citation
+------------------
+
+mjlab is licensed under the Apache License, Version 2.0.
+Please refer to the `LICENSE file <https://github.com/mujocolab/mjlab/blob/main/LICENSE/>`_ for details.
+
+If you use mjlab in your research, we would appreciate a citation:
+
+.. code-block:: bibtex
+
+    @article{Zakka_mjlab_A_Lightweight_2026,
+        author = {Zakka, Kevin and Liao, Qiayuan and Yi, Brent and Le Lay, Louis and Sreenath, Koushil and Abbeel, Pieter},
+        title = {{mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning}},
+        url = {https://arxiv.org/abs/2601.22074},
+        year = {2026}
+    }
+
+Acknowledgments
+---------------
+
+mjlab would not exist without the excellent work of the Isaac Lab team, whose API design
+and abstractions mjlab builds upon.
+
+Thanks also to the MuJoCo Warp team — especially Erik Frey and Taylor Howell — for
+answering our questions, giving helpful feedback, and implementing features based
+on our requests countless times.