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Upstream: https://github.com/michaelgillett/mjlab Upstream-Commit: c19f713c415a699a79d71cd96aa13c3104a05047 Upstream-Branch: main
89 lines
3.5 KiB
ReStructuredText
89 lines
3.5 KiB
ReStructuredText
.. _motivation:
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Why mjlab?
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==========
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Reinforcement learning has become a powerful tool for training robot
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controllers in simulation and transferring them to real hardware. The
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fidelity of this pipeline hinges on getting simulation details right.
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Several frameworks address this.
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`Isaac Lab <https://github.com/isaac-sim/IsaacLab>`_
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provides a comprehensive manager-based API for composing RL environments,
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but requires the Omniverse runtime, which adds installation complexity and
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startup latency.
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`MuJoCo Playground <https://playground.mujoco.org/>`_ takes the opposite
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approach: minimal
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abstractions and monolithic environment definitions that are easy to hack
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and quick to prototype, but code duplication across robots and tasks makes
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multi-task codebases difficult to maintain. There remains a gap for a
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framework that is both lightweight and built on a proven orchestration API
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with access to best-in-class physics.
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mjlab fills this gap. It adopts Isaac Lab's manager-based design, where
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users compose self-contained building blocks for observations, rewards,
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events, and commands, and pairs it with MuJoCo Warp for GPU-accelerated
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physics simulation. The result is a framework with minimal dependencies,
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fast startup, direct access to native MuJoCo model and data structures,
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and a PyTorch-native interface for policy training.
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Design philosophy
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-----------------
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mjlab is designed around three core engineering commitments:
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1. **Minimal installation friction.** A single
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``uvx --from mjlab --refresh demo`` command is enough to run the
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framework. No heavyweight runtimes, no multi-gigabyte downloads. The
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dependency footprint is kept intentionally small.
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2. **Transparent and inspectable physics.** mjlab targets a single physics
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stack, MuJoCo Warp, to prioritize simulation transparency and
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debuggability. The framework exposes MuJoCo-native ``MjModel`` and
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``MjData`` structures for direct inspection and state access.
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Cross-simulator portability is a non-goal; mjlab favors precise control
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and interpretability over backend generality.
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3. **Tight MuJoCo ecosystem integration.** Users work directly with MuJoCo
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models and conventions. MJCF files, MuJoCo Menagerie assets, and
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standard MuJoCo tooling all work without translation layers.
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Scope
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-----
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mjlab provides infrastructure for rigid-body robot learning. It includes
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depth and raycast sensors for geometric perception. High-fidelity RGB
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rendering is out of scope. This does not preclude vision-based policies:
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a common approach is to train privileged policies using full state, then
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distill into vision-based controllers using external rendering.
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The framework is intended to be extended to custom robots, tasks, sensors,
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and actuators. It ships with reference implementations of velocity tracking,
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motion imitation, and manipulation tasks.
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Comparison
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----------
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.. list-table::
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:header-rows: 1
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:widths: 25 25 50
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* - Framework
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- Strengths
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- Best for
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* - **mjlab**
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- Lightweight, fast iteration, native MuJoCo, PyTorch
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- MuJoCo users who want structured RL environments with GPU acceleration
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* - **Isaac Lab**
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- Photorealistic rendering, USD pipeline, Omniverse ecosystem
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- Projects that need Isaac Sim capabilities
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* - **MuJoCo Playground**
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- Minimal abstractions, easy to hack, quick prototyping
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- One-off experiments and rapid iteration on single tasks
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* - **Newton**
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- Multi-physics solvers (deformables, VBD), differentiable simulation
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- Projects that need solver flexibility beyond rigid-body MuJoCo
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