Menagerie is a collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.

A physics simulator is only as good as the model it is simulating, and in a powerful simulator like MuJoCo with many modeling options, it is easy to create "bad" models which do not behave as expected. The goal of this collection is to provide the community with a curated library of well-designed models that work well right out of the gate.

• Getting Started
  • Prerequisites
  • Overview
  • Usage
    • Via robot-descriptions
    • Via git clone
• Model Quality and Contributing
  • Quick contributor setup
• Menagerie Models
• Citing Menagerie
• Acknowledgments
• Changelog
• License and Disclaimer

Getting Started

Prerequisites

The minimum required MuJoCo version for each model is specified in its respective README. You can download prebuilt binaries for MuJoCo from the GitHub releases page, or if you are working with Python, you can install the native bindings from PyPI via pip install mujoco. For alternative installation instructions, see here.

Overview

The structure of Menagerie is illustrated below. For brevity, we have only included one model directory since all others follow the exact same pattern.

├── unitree_go2
│   ├── assets
│   │   ├── base_0.obj
│   │   ├── ...
│   ├── go2.png
│   ├── go2.xml
│   ├── LICENSE
│   ├── README.md
│   └── scene.xml
│   └── go2_mjx.xml
│   └── scene_mjx.xml

• assets: stores the 3D meshes (.stl or .obj) of the model used for visual and collision purposes
• LICENSE: describes the copyright and licensing terms of the model
• README.md: contains detailed steps describing how the model's MJCF XML file was generated
• <model>.xml: contains the MJCF definition of the model
• scene.xml: includes <model>.xml with a plane, a light source and potentially other objects
• <model>.png: a PNG image of scene.xml
• <model>_mjx.xml: contains an MJX-compatible version of the model. Not all models have an MJX variant.
• scene_mjx.xml: same as scene.xml but loads the MJX variant

Note that <model>.xml solely describes the model, i.e., no other entity is defined in the kinematic tree. We leave additional body definitions for the scene.xml file, as can be seen in the Shadow Hand scene.xml.

Usage

Via robot-descriptions

You can use the opensource robot_descriptions package to load any model in Menagerie. It is available on PyPI and can be installed via pip install robot_descriptions.

Once installed, you can load a model of your choice as follows:

import mujoco

# Loading a specific model description as an imported module.
from robot_descriptions import panda_mj_description
model = mujoco.MjModel.from_xml_path(panda_mj_description.MJCF_PATH)

# Directly loading an instance of MjModel.
from robot_descriptions.loaders.mujoco import load_robot_description
model = load_robot_description("panda_mj_description")

# Loading a variant of the model, e.g. panda without a gripper.
model = load_robot_description("panda_mj_description", variant="panda_nohand")

Via git clone

You can also directly clone this repository in the directory of your choice:

git clone https://github.com/google-deepmind/mujoco_menagerie.git

You can then interactively explore the model using the Python viewer:

python -m mujoco.viewer --mjcf mujoco_menagerie/unitree_go2/scene.xml

If you have further questions, please check out our FAQ.

Model Quality and Contributing

Our goal is to eventually make all Menagerie models as faithful as possible to the real system they are being modeled after. Improving model quality is an ongoing effort, and the current state of many models is not necessarily as good as it could be.

However, by releasing Menagerie in its current state, we hope to consolidate and increase visibility for community contributions. To help Menagerie users set proper expectations around the quality of each model, we introduce the following grading system:

Grade	Description
A+	Values are the product of proper system identification
A	Values are realistic, but have not been properly identified
B	Stable, but some values are unrealistic
C	Conditionally stable, can be significantly improved

The grading system will be applied to each model once a proper system identification toolbox is created. We are currently planning to release this toolbox later this year.

For more information regarding contributions, for example to add a new model to Menagerie, see CONTRIBUTING.

Quick contributor setup

Two commands and you're set up. You need uv installed; that's the only prerequisite.

make install   # one-time: installs pre-commit + git hook
make all       # run every check CI runs (lint + format + license + XML + tests)

After make install, the fast checks fire automatically on every git commit. Run make all before pushing. See CONTRIBUTING.md for the full breakdown.

Menagerie Models

Click any thumbnail below to open the model in an in-browser MuJoCo viewer powered by live.mujoco.org.

Humanoids.

Preview	Name	DoFs	License
	Unitree H1	19	BSD-3-Clause
	Robotis OP3	20	Apache-2.0
	Unitree G1	29	BSD-3-Clause
	TALOS	44	Apache-2.0
	Booster T1	23	Apache-2.0
	ToddlerBot 2XC	44	MIT
	PNDbotics Adam_lite	25	MIT
	Apptronik Apollo	32	Apache-2.0
	Berkeley Humanoid	12	BSD-3-Clause
	Fourier N1	23	Apache-2.0
	ToddlerBot 2XM	44	MIT

Quadrupeds.

Preview	Name	DoFs	License
	Unitree A1	12	BSD-3-Clause
	Google Barkour v0	12	Apache-2.0
	ANYmal B	12	BSD-3-Clause
	Unitree Go1	12	BSD-3-Clause
	ANYmal C	12	BSD-3-Clause
	Google Barkour vB	12	Apache-2.0
	Unitree Go2	12	BSD-3-Clause
	Boston Dynamics Spot	19	BSD-3-Clause

Bipeds.

Preview	Name	DoFs	License
	Agility Cassie	28	MIT

Biomechanical.

Preview	Name	DoFs	License
	Flybody	102	Apache-2.0
	IIT SoftFoot	92	BSD-3-Clause
	MS-Human-700	85	Apache-2.0

Dual Arms.

Preview	Name	DoFs	License
	ALOHA	16	BSD-3-Clause

Mobile Manipulators.

Preview	Name	DoFs	License
	Google Robot	9	Apache-2.0
	Hello Robot Stretch 2	17	BSD-3-Clause-Clear
	Stanford TidyBot	18	MIT
	Hello Robot Stretch 3	20	Apache-2.0
	TIAGo	22	Apache-2.0
	TIAGo++	25	Apache-2.0

Drones.

Preview	Name	DoFs	License
	Skydio X2	0	Apache-2.0
	Bitcraze Crazyflie 2	0	MIT

Arms.

Preview	Name	DoFs	License
	Franka Emika Panda	9	Apache-2.0
	Franka Robotics FR3	7	Apache-2.0
	Lite 6	6	BSD-3-Clause
	Unitree Z1	6	BSD-3-Clause
	Universal Robots UR5e	6	BSD-3-Clause
	Rethink Robotics Sawyer	7	Apache-2.0
	Universal Robots UR10e	6	BSD-3-Clause
	KUKA LBR iiwa 14	7	BSD-3-Clause
	ViperX 300 6DOF	8	BSD-3-Clause
	xArm7	13	BSD-3-Clause
	Kinova Gen3	7	BSD-3-Clause
	AgileX PiPER	8	MIT
	Flexiv Robotics Rizon4	7	Apache-2.0
	ARX L5	8	BSD-3-Clause
	Flexiv Robotics Rizon4S	7	Apache-2.0
	WidowX 250 6DOF	8	BSD-3-Clause
	Standard Open Arm-100 5DOF - Version 1.3	6	Apache-2.0
	Low-Cost Robot Arm	6	Apache-2.0
	Yet Another Manipulator (YAM)	8	MIT

End-effectors.

Preview	Name	DoFs	License
	Panda Gripper	2	Apache-2.0
	Allegro Hand V3	16	BSD-2-Clause
	Shadow Hand E3M5	24	Apache-2.0
	Robotiq 2F-85	8	BSD-2-Clause
	xarm7 Gripper	6	BSD-3-Clause
	Shadow DEX-EE Hand	12	Apache-2.0
	Leap Hand	16	MIT
	UMI-Gripper	8	MIT
	Sharpa Wave	22	Apache-2.0

Mobile Bases.

Preview	Name	DoFs	License
	Robot soccer kit omnidirectional	64	MIT

Miscellaneous.

Preview	Name	DoFs	License
	Realsense D435i	0	Apache-2.0

Citing Menagerie

If you use Menagerie in your work, please use the following citation:

@software{menagerie2022github,
  author = {Zakka, Kevin and Tassa, Yuval and {MuJoCo Menagerie Contributors}},
  title = {{MuJoCo Menagerie: A collection of high-quality simulation models for MuJoCo}},
  url = {http://github.com/google-deepmind/mujoco_menagerie},
  year = {2022},
}

Acknowledgments

The models in this repository are based on third-party models designed by many talented people, and would not have been possible without their generous open-source contributions. We would like to acknowledge all the designers and engineers who made MuJoCo Menagerie possible.

We'd like to thank Pedro Vergani for his help with visuals and design.

The main effort required to make this repository publicly available was undertaken by Kevin Zakka, with help from the Robotics Simulation team at Google DeepMind.

This project has also benefited from contributions by members of the broader community — see the CONTRIBUTORS.md for a full list.

Changelog

For a summary of key updates across the repository, see the global CHANGELOG.md.

Each individual model also includes its own CHANGELOG.md file with model-specific updates, linked directly from the corresponding README.

License and Disclaimer

XML and asset files in each individual model directory of this repository are subject to different license terms. Please consult the LICENSE files under each specific model subdirectory for the relevant license and copyright information.

All other content is Copyright 2022 DeepMind Technologies Limited and licensed under the Apache License, Version 2.0. A copy of this license is provided in the top-level LICENSE file in this repository. You can also obtain it from https://www.apache.org/licenses/LICENSE-2.0.

This is not an officially supported Google product.