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
• Menagerie Models
• Citing Menagerie
• Acknowledgments
• 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 (see Menagerie Models for more information).
• 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.

Menagerie Models

Arms.

Name	Maker	DoFs	License	MJX
FR3	Franka Robotics	7	Apache-2.0	✖️
iiwa14	KUKA	7	BSD-3-Clause	✖️
Lite6	UFACTORY	6	BSD-3-Clause	✖️
Panda	Franka Robotics	7	BSD-3-Clause	✔️
Sawyer	Rethink Robotics	7	Apache-2.0	✖️
Unitree Z1	Unitree Robotics	6	BSD-3-Clause	✖️
UR5e	Universal Robots	6	BSD-3-Clause	✖️
UR10e	Universal Robots	6	BSD-3-Clause	✖️
ViperX 300	Trossen Robotics	8	BSD-3-Clause	✖️
WidowX 250	Trossen Robotics	8	BSD-3-Clause	✖️
xarm7	UFACTORY	7	BSD-3-Clause	✖️
Gen3	Kinova Robotics	7	BSD-3-Clause	✖️
SO-ARM100	The Robot Studio	5	Apache-2.0	✖️

Bipeds.

Name	Maker	DoFs	License	MJX
Cassie	Agility Robotics	28	BSD-3-Clause	✖️

Dual Arms.

Name	Maker	DoFs	License	MJX
ALOHA 2	Trossen Robotics, Google DeepMind	16	BSD-3-Clause	✔️

Drones.

Name	Maker	DoFs	License	MJX
Crazyflie 2	Bitcraze	0	MIT	✖️
Skydio X2	Skydio	0	Apache-2.0	✖️

End-effectors.

Name	Maker	DoFs	License	MJX
Allegro Hand V3	Wonik Robotics	16	BSD-2-Clause	✖️
LEAP Hand	Carnegie Mellon University	16	MIT	✖️
Robotiq 2F-85	Robotiq	8	BSD-2-Clause	✖️
Shadow Hand EM35	Shadow Robot Company	24	Apache-2.0	✖️
Shadow DEX-EE Hand	Shadow Robot Company	12	Apache-2.0	✖️

Mobile Manipulators.

Name	Maker	DoFs	License	MJX
Google Robot	Google DeepMind	9	Apache-2.0	✖️
Stretch 2	Hello Robot	17	Clear BSD	✖️
Stretch 3	Hello Robot	17	Apache-2.0	✖️
PAL Tiago	PAL Robotics	12	Apache-2.0	✖️
PAL Tiago Dual	PAL Robotics	21	Apache-2.0	✖️

Humanoids.

Name	Maker	DoFs	License	MJX
Robotis OP3	Robotis	20	Apache-2.0	✖️
Unitree G1	Unitree Robotics	37	BSD-3-Clause	✖️
Unitree H1	Unitree Robotics	19	BSD-3-Clause	✖️
TALOS	PAL Robotics	32	Apache-2.0	✖️

Quadrupeds.

Name	Maker	DoFs	License	MJX
ANYmal B	ANYbotics	12	BSD-3-Clause	✖️
ANYmal C	ANYbotics	12	BSD-3-Clause	✔️
Spot	Boston Dynamics	12	BSD-3-Clause	✖️
Unitree A1	Unitree Robotics	12	BSD-3-Clause	✖️
Unitree Go1	Unitree Robotics	12	BSD-3-Clause	✖️
Unitree Go2	Unitree Robotics	12	BSD-3-Clause	✔️
Google Barkour v0	Google DeepMind	12	Apache-2.0	✔️
Google Barkour vB	Google DeepMind	12	Apache-2.0	✔️

Biomechanical.

Name	Maker	DoFs	License	MJX
flybody	Google DeepMind, HHMI Janelia Research Campus	102	Apache-2.0	✖️

Miscellaneous.

Name	Maker	DoFs	License	MJX
D435i	Intel Realsense	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.

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.