unitreerobotics/unitree_rl_gym

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3516 stars · 580 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A reinforcement learning gym environment for training locomotion policies on Unitree robots (Go2, H1, H1_2, G1) using Isaac Gym and Mujoco. It provides a train → play → sim2sim → sim2real workflow for deploying learned motion control to physical robots.

Use cases

• train locomotion policies for Unitree quadruped and humanoid robots
• reinforcement learning for legged robot motion control
• sim-to-real transfer of robot control policies
• train walking policies in Isaac Gym and deploy to hardware
• validate RL policies across simulators with sim2sim
• learn reinforcement learning for robotics with real robots

When to choose

• you own or target Unitree Go2, H1, or G1 robots
• you want an end-to-end RL-to-real-robot pipeline for legged locomotion
• you need GPU-parallel training environments for robot policies

When to avoid

• you need RL for non-Unitree robots or manipulators
• you only want simulation without real-robot deployment
• you lack an NVIDIA GPU required by Isaac Gym

Facets

library · maturity active

reinforcement-learning simulation robotics machine-learning llm-training robotics reinforcement-learning simulation machine-learning python cross-platform legged-robots sim2real isaac-gym mujoco motion-control unitree gym-environment quadruped humanoid linux gpu

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