robfiras/loco-mujoco

Health v2 · maintenance only

Adoption not part of the score

1453 stars · 154 forks observed · 2026-08-28

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

LocoMuJoCo is an imitation learning benchmark for whole-body locomotion control built on MuJoCo, featuring humanoid, quadruped, and musculoskeletal environments with retargeted motion capture datasets. It also supports reinforcement learning via custom rewards and includes JIT-compiled JAX reference algorithms like PPO, GAIL, AMP, and DeepMimic.

Use cases

• benchmark imitation learning algorithms for locomotion
• train humanoid locomotion policies from motion capture data
• run GAIL or AMP on quadruped environments
• retarget motion capture datasets between robots
• compare trajectories with dynamic time warping metrics
• train RL agents with domain and terrain randomization

When to choose

• you need a standardized benchmark for whole-body imitation learning
• you want motion-capture datasets pre-retargeted to humanoid or quadruped models
• you need fast GPU-parallel simulation via MJX/MjWarp
• you want both RL and IL support in one locomotion suite

When to avoid

• you need manipulation or non-locomotion tasks
• you require a non-Python or non-JAX workflow
• you need real robot deployment out of the box

Facets

library · maturity active

machine-learning reinforcement-learning simulation benchmarking machine-learning robotics simulation artificial-intelligence python imitation-learning mujoco locomotion mocap jax gymnasium biomechanics humanoids quadrupeds dataset linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="robfiras/loco-mujoco")