Gymnasium-Robotics

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Gymnasium-Robotics is an AI Agents & Automation tool that provides a collection of robotics simulation environments for reinforcement learning. It uses the Gymnasium API and MuJoCo physics engine.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Gymnasium-Robotics?

Gymnasium-Robotics offers a comprehensive suite of robotics simulation environments designed for reinforcement learning research and development. Built upon the Gymnasium API and leveraging the MuJoCo physics engine, it provides a standardized platform for training AI agents. The library includes diverse environments such as Fetch robot manipulation tasks, Shadow Dexterous Hand object manipulation, and multi-agent factorizations with MaMuJoCo. It also features D4RL environments, including maze navigation and Adroit Arm tasks. A key differentiator is its extension of the core Gymnasium API with the GoalEnv class, enabling advanced functionalities like Hindsight Experience Replay (HER) through re-computable reward, terminated, and truncated signals.

Best used for

Ideal for developers and data scientists who need to simulate complex robotic tasks, train reinforcement learning agents, and conduct research in robotics. Especially valuable for those working with MuJoCo and seeking standardized environments for manipulation, navigation, and multi-agent learning.

Common actions

simulate robotics

train AI agents

develop reinforcement learning

research robotics

face swappinglow-code/no-code"AI Agents"github copilotcollaborationdeepfakeautomated workflowworkflowsopen-source

Capabilities

Key features

MuJoCo physics engine
Gymnasium API extension
Fetch robot environments
Shadow Dexterous Hand environments
D4RL environments
Multi-agent factorizations
Hindsight Experience Replay support

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What physics engine does Gymnasium-Robotics use?

Gymnasium-Robotics utilizes the MuJoCo physics engine from Deepmind, along with its maintained Python bindings. Users can also install an older version of the library to use mujoco-py if preferred, though the latest environments are designed for the official MuJoCo bindings.

What types of robotic environments are included?

The library includes a variety of environments such as Fetch robot manipulation tasks (Reach, Push, Pick and Place), Shadow Dexterous Hand object manipulation, multi-agent MuJoCo environments, D4RL environments like mazes, and Adroit Arm tasks (nail hammering, door opening).

How does the GoalEnv API extension work?

The GoalEnv class extends the Gymnasium API by requiring a dictionary observation space with 'observation', 'desired_goal', and 'achieved_goal' keys. It also exposes functions to re-compute reward, terminated, and truncated signals, which is crucial for algorithms like Hindsight Experience Replay (HER).

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