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 underparams/andmicroduck_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) |
Upstream
- pollen-robotics/microduck
- pollen-robotics/microduck_rl (Apache-2.0)