Alessio
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Every step, the flex block of the implicit effective metric M + K was factorized by sparse Cholesky, because K depends on the configuration. On model/flex/bag.xml, added here, that is roughly half the step, against a comparable share for the constraint solve it exists to accelerate. Keep only the metric's per-vertex 3x3 diagonal blocks, prefactored. Neither consumer needs the exact inverse: the CG constraint solver only wants a preconditioner, and qacc_smooth can come from an iterative solve using those blocks. They are O(n) to build and to apply, but weaker, so CG runs about twice the iterations and qacc_smooth becomes an iteration rather than a direct solve. Net, the bag model steps roughly twice as fast. The preconditioner, by metric state. Inactive, meaning no flex elasticity or an explicit integrator: M^-1, unchanged. Bending only (nefmK == 0): M^-1 plus the exact constant bending factor from mj_setConst on the dofs it covers, unchanged; that factor is built at model compile time and costs nothing per step. Per-step stiffness: M^-1 plus the 3x3 blocks, where before it was a per-step sparse Cholesky, or, when M couples across the flex block, an inner PCG of up to 50 iterations run once per outer CG iteration. Only models carrying per-step stretch stiffness change in wall-clock. Both ponchos hold their timing and take slightly fewer CG iterations than before, because the preconditioner is now symmetric: it applies M^-1 and the covered blocks to disjoint sets of dofs, where previously the two overlapped and the operator was not symmetric, which PCG requires. mjd_effSolve is the accurate solve of (M + K)x = b; what used to carry that name only preconditions and is now mjd_effPrec. Its CG guarded the division by pAp with mjMINVAL, an absolute floor on a quantity that scales with the square of the right-hand side, so a small b aborted the solve while the curvature was healthy: four flex models were quietly left short of tolerance. For an SPD metric the guard is positivity, and with that the same solves converge. The qacc_smooth call site in mj_fwdAcceleration is textually unchanged but now reaches the iterative solve, which converges on opt.tolerance rather than a hardcoded threshold, floored in mjUSESINGLE builds where the squared target is unreachable in float. Reaching the iteration cap names the ill-conditioned flex stiffness and then reports it through mjWARN_INERTIA, rather than returning an under-converged result. Covered dofs are located by walking the covered rows of the stiffness matrix, as they need not be 3-aligned from dof 0: any joint declared before a flexcomp shifts them. mjData.efm_L_rownnz, efm_L_rowadr and efm_L_colind described the sparsity of the deleted factorization and are removed: left NULL with nonzero mjxmacro extents they made the Python bindings hand back uninitialized arrays. efm_active loses the value 2 for the same reason, nothing selects a solve path on preconditioner exactness any more. Both are recorded under breaking changes. model/flex/bag.xml is added because no shipped model carried per-step stretch stiffness. The ponchos are bending-only and trampoline.xml uses an explicit integrator, so the metric never activates there. It is excluded from WriteReadCompareTest: stretch stiffness amplifies rest geometry that XML rounds on save.
MuJoCo stands for Multi-Joint dynamics with Contact. It is a general purpose physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, machine learning, and other areas which demand fast and accurate simulation of articulated structures interacting with their environment.
This repository is maintained by Google DeepMind.
MuJoCo has a C API and is intended for researchers and developers. The runtime simulation module is tuned to maximize performance and operates on low-level data structures that are preallocated by the built-in XML compiler. The library includes interactive visualization with a native GUI, rendered in OpenGL. MuJoCo further exposes a large number of utility functions for computing physics-related quantities.
We also provide Python bindings and a plug-in for the Unity game engine.
Documentation
MuJoCo's documentation can be found at mujoco.readthedocs.io. Upcoming features due for the next release can be found in the changelog in the "latest" branch.
Getting Started
There are two easy ways to get started with MuJoCo:
-
Run
simulateon your machine. This video shows a screen capture ofsimulate, MuJoCo's native interactive viewer. Follow the steps described in the Getting Started section of the documentation to getsimulaterunning on your machine. -
Explore our online IPython notebooks. If you are a Python user, you might want to start with our tutorial notebooks running on Google Colab:
- The introductory tutorial teaches MuJoCo basics:
- The Model Editing tutorial shows how to create and edit models procedurally:
- The rollout tutorial shows how to use the multithreaded
rolloutmodule: - The LQR tutorial synthesizes a linear-quadratic controller, balancing a
humanoid on one leg:
- The least-squares tutorial explains how to use the Python-based nonlinear
least-squares solver:
- The MJX tutorial provides usage examples of
MuJoCo XLA, a branch of MuJoCo written in JAX:
- The differentiable physics tutorial trains locomotion policies with
analytical gradients automatically derived from MuJoCo's physics step:
Installation
Prebuilt binaries
Versioned releases are available as precompiled binaries from the GitHub releases page, built for Linux (x86-64 and AArch64), Windows (x86-64 only), and macOS (universal). This is the recommended way to use the software.
Building from source
Users who wish to build MuJoCo from source should consult the build from
source section of the documentation. However, note that the commit at
the tip of the main branch may be unstable.
Python (>= 3.10)
The native Python bindings, which come pre-packaged with a copy of MuJoCo, can be installed from PyPI via:
pip install mujoco
Note that Pre-built Linux wheels target manylinux2014, see
here for compatible distributions. For more
information such as building the bindings from source, see the Python bindings
section of the documentation.
Versioning
We aim to release MuJoCo in the first week of each month. Our versioning standards changed to modified Semantic Versioning in 3.5.0, see versioning for details.
Contributing
We welcome community engagement: questions, requests for help, bug reports and feature requests. To read more about bug reports, feature requests and more ambitious contributions, please see our contributors guide and style guide.
Asking Questions
Questions and requests for help are welcome as a GitHub "Asking for Help" Discussion and should focus on a specific problem or question.
Bug reports and feature requests
GitHub Issues are reserved for bug reports, feature requests and other development-related subjects.
Related software
MuJoCo is the backbone for numerous environment packages. Below we list several bindings and converters.
Bindings
These packages give users of various languages access to MuJoCo functionality:
First-party bindings:
- Python bindings
- dm_control, Google DeepMind's related environment stack, includes PyMJCF, a module for procedural manipulation of MuJoCo models.
- JavaScript bindings and WebAssembly support (inspired stillonearth and zalo's community projects; mjswan extends these with real-time policy control, interactive force application, and more).
- C# bindings and Unity plug-in
Third-party bindings:
- MATLAB Simulink: Simulink Blockset for MuJoCo Simulator by Manoj Velmurugan.
- Swift: swift-mujoco
- Java: mujoco-java
- Julia: MuJoCo.jl
- Rust: MuJoCo-rs
Converters
- OpenSim: MyoConverter converts OpenSim models to MJCF.
- SDFormat: gz-mujoco is a two-way SDFormat <-> MJCF conversion tool.
- OBJ: obj2mjcf a script for converting composite OBJ files into a loadable MJCF model.
- onshape: Onshape to Robot Converts onshape CAD assemblies to MJCF.
Citation
If you use MuJoCo for published research, please cite:
@inproceedings{todorov2012mujoco,
title={MuJoCo: A physics engine for model-based control},
author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},
booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages={5026--5033},
year={2012},
organization={IEEE},
doi={10.1109/IROS.2012.6386109}
}
License and Disclaimer
Copyright 2021 DeepMind Technologies Limited.
Box collision code (engine_collision_box.c)
is Copyright 2016 Svetoslav Kolev.
ReStructuredText documents, images, and videos in the doc directory are made
available under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0)
license. You may obtain a copy of the License at
https://creativecommons.org/licenses/by/4.0/legalcode.
Source code is licensed under the Apache License, Version 2.0. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.
This is not an officially supported Google product.