ZiwenZhuang/parkour

Health v2 · maintenance only

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Adoption not part of the score

1114 stars · 151 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official code for 'Robot Parkour Learning' (CoRL 2023), a reinforcement learning system that trains quadrupedal robots to perform vision-based parkour skills like climbing, leaping, and crawling. It provides IsaacGym simulation environments, RL training and distillation code, and deployment instructions for Unitree Go1 and Go2 robots.

Use cases

• train quadruped robots to climb and jump obstacles in simulation
• learn vision-based locomotion policies with reinforcement learning
• deploy RL-trained policies on a Unitree Go1 or Go2 robot
• generate parkour skills without reference motion data
• distill privileged RL policies into depth-camera-based policies
• research sim-to-real transfer for legged robots

When to choose

• you want to reproduce or extend the CoRL 2023 robot parkour results
• you need IsaacGym-based training environments for legged locomotion
• you own a Unitree Go1/Go2 and want to run vision-based parkour policies
• you are researching RL skill generation and policy distillation for quadrupeds

When to avoid

• you need a production-ready robotics framework rather than research code
• you work with non-quadruped robots or humanoids
• you lack a GPU or NVIDIA IsaacGym-compatible setup
• you need plug-and-play deployment without simulation training

Facets

library · maturity active

reinforcement-learning simulation robotics machine-learning robotics reinforcement-learning machine-learning simulation python legged-locomotion quadruped-robots isaacgym sim-to-real parkour unitree-go1 unitree-go2 depth-camera policy-distillation research-code linux gpu

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markdown · JSON · MCP: product_card(name="ZiwenZhuang/parkour")