MicroDuck RL โ€” Velocity Walking (trained 4096 env x 6000 PPO)

Self-trained walking baseline for the open-source MicroDuck biped (Duck Together / RoboGo cloud project).

  • Task: Mjlab-Velocity-Flat-MicroDuck (flat-ground velocity tracking)
  • Training: mjlab + MuJoCo Warp + PPO, 4096 parallel envs x 6000 iterations, ~1h59m on NVIDIA RTX 5090
  • Contract: ONNX [1, 61] -> [1, 14] (observation normalizer baked in via scripts/export.py)
  • Result: mean reward ~81.5, mean episode length ~919.9, no NaN
  • Note: this is an intermediate baseline (stable stand/walk, weaker straight-line speed tracking than the official alpha_walking). Full training config/log diff included under params/ and microduck_rl.diff.

Files

File Description
model_5999.pt final PyTorch checkpoint
final.onnx exported ONNX policy [1,61] -> [1,14]
params/agent.yaml, params/env.yaml training configuration
microduck_rl.diff upstream repo diff (commit pin)

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