mujoco

Multi-Joint dynamics with Contact. A general purpose physics simulator.

An elegant PyTorch deep reinforcement learning library.

Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

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

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

Isaac Lab API, powered by MuJoCo-Warp, for RL and robotics research

A fully open-source humanoid arm for physical AI research and deployment in contact-rich environments.

Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

Real-time behaviour synthesis with MuJoCo, using Predictive Control

Python inverse kinematics based on MuJoCo.

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

MyoSuite is a collection of environments/tasks to be solved by musculoskeletal models simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API.

Training a humanoid robot for locomotion using Reinforcement Learning

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

The Fastest Deep Reinforcement Learning Library

A collection of robotics simulation environments for reinforcement learning

Python library for Reinforcement Learning.

UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms

Unified Reinforcement Learning Framework

A unified framework for robot learning

Converting Onshape assembly to robot definition (URDF, SDF, MuJoCo) through Onshape API

ROS2-Control implementations for Quadruped robots, include sim2real

MuJoCo fruit fly body model and locomotion RL tasks

A high-fidelity, open-source robotics simulator integrating Unreal Engine's photorealistic rendering with MuJoCo's precision physics.

A model predictive controller for quadruped robots based on the single rigid body model and written in python. Gradient-based (acados) or Sampling-based (jax)