rohanpsingh/LearningHumanoidWalking

Health v2 · maintenance only

Adoption not part of the score

1207 stars · 135 forks observed · 2026-08-28

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

A Python research codebase for training humanoid robots to walk using deep reinforcement learning (PPO) in MuJoCo simulation. It provides extensible environments, task/reward definitions, and robot abstractions for models like JVRC-1 and Unitree H1, accompanying peer-reviewed papers on robust bipedal locomotion.

Use cases

• train a humanoid robot to walk with reinforcement learning
• simulate bipedal locomotion in mujoco
• learn ppo policies for humanoid walking
• add a custom humanoid robot to rl locomotion training
• reproduce humanoid walking research papers
• train walking policies for jvrc-1 or unitree h1

When to choose

• You want to train bipedal or humanoid locomotion policies with PPO in MuJoCo
• You need a reference implementation tied to published humanoid walking research
• You want a structured, extensible codebase for adding your own humanoid robot environment

When to avoid

• You need a general-purpose RL library with many algorithms beyond PPO
• You are working on quadrupeds, manipulators, or non-humanoid robot locomotion
• You need a production robotics middleware or real-time control stack rather than training code

Facets

library · maturity active

reinforcement-learning simulation machine-learning robotics reinforcement-learning simulation machine-learning python windows humanoid-robots bipedal-locomotion mujoco ppo jvrc-1 unitree-h1 sim-to-real research-code physics-simulation robot-controllers linux macos

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