mujaco-gym-lite 1.0.2

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Mujaco Gym Lite (Jaco2 + MuJoCo)

mujaco-gym-lite is a lightweight task suite for robotic manipulation with the Kinova Jaco2 arm in MuJoCo.

It includes:

• MuJoCo-based manipulation tasks (drawer, button, door, assembly ring, hinged box, lever, soccer, lidded box)
• Example scripts for scripted demos and environment debugging

Prerequisites

• Linux (this repository is developed and tested on Linux)
• Python 3.10
• MuJoCo Python bindings (mujoco) available in your environment
• OpenGL/GLFW runtime libraries for rendering

If you run with local rendering and miss system libraries, install them on Ubuntu/Debian:

sudo apt update
sudo apt install -y libglfw3 libgl1 libosmesa6 libopenblas0

Installation

Install from PyPI (recommended)

python3 -m venv .venv
source .venv/bin/activate

pip install --upgrade pip setuptools wheel
pip install mujaco-gym-lite

# Required for MuJoCo tasks
pip install mujoco "gymnasium[mujoco]"

Install from source (development)

git clone <your-fork-or-this-repo-url>
cd mujaco-gym-lite

python3 -m venv .venv
source .venv/bin/activate

pip install --upgrade pip setuptools wheel
pip install -e .

# Required for MuJoCo tasks
pip install mujoco "gymnasium[mujoco]"

Docker (optional)

CUDA 12.6 Docker scripts are included in dockerfiles/cuda126.

cd dockerfiles/cuda126
./build.sh
./launch.sh

Quick Start

Run any demo from the repository root.

python examples/run_drawer_open_demo.py
python examples/run_assembly_ring_demo.py
python examples/run_soccer_demo.py

Some demos support options such as recording:

python examples/run_drawer_open_demo.py --render_mode rgb_array --record

Demo GIF Gallery

Recorded demos converted from examples/demo_result and stored in docs/demo_gifs.

Assembly Ring

Button

Drawer

Hinged Box Open

Lever Pull

Lidded Box Reach

Sliding Door

Soccer

Available Environments

Registered Gymnasium IDs:

• DrawerRobot-v0
• ButtonRobot-v0
• SlidingDoorRobot-v0
• AssemblyRingRobot-v0
• HingedBoxRobot-v0
• LeverRobot-v0
• SoccerRobot-v0
• LiddedBoxRobot-v0

Actions, States, and Rewards

Actions

MuJoCo robot environments use gymnasium.spaces.Dict action spaces. Typical keys include:

• robot/end_effector/position (3D target position)
• robot/end_effector/rotation (quaternion)
• robot/home (home command flag)
• camera/<name>/position, camera/<name>/rotation (for mocap camera control)

Exact keys can vary by task and wrappers.

States (Observations)

Observations are also dictionaries and usually include:

• Robot state (end-effector pose, joint angles, contacts)
• Task/object state (for example object pose, handle position, joint positions)
• Camera outputs when enabled (color/depth/segmentation and camera parameters)
• Low-level simulator state (qpos, qvel)

Rewards

Rewards are task-specific and generally dense/shaped to support learning. For many manipulation tasks, reward terms combine:

• Progress toward the task goal (for example object alignment or opening distance)
• Control/interaction quality (for example grasp or caging quality)
• A success-related bonus when the task condition is met

Additional reward diagnostics are exposed through the info dictionary (for example reward/* keys and task/success).

Repository Layout

• mujaco_gym_lite/: main package (environments, builders, wrappers, tools)
• examples/: runnable demo scripts
• assets/: object meshes, robot files, textures
• dockerfiles/: Docker build/run scripts

License

Source code in this repository (excluding asset content) is licensed under the MIT License.

Asset licensing and attribution may differ by source. Please check the files inside the asset directories:

• assets/objects/metaworld/README.md
• assets/robots/README.md
• assets/textures/README.md

Use those asset files as the primary reference for origin and license/usage terms.