donaldmicroduck-v2
New strategy, slightly better result. Hear me quack!
A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz, on rollers). Runs until told otherwise — a gait, loaded into a policy slot.
Run it on a robot
sudo robotctl policy load <slot> mrharm/microduck-donaldmicroduck-v2
The observation normalizer is baked into policy.onnx; feed raw observations.
manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo).
Training
- task_id:
Mjlab-RollerSprint2m-MicroDuck - repo:
https://github.com/pollen-robotics/microduck-challenges - branch:
main - commit:
3cc03f144 - checkpoint:
2250 - seed:
6969 - base:
mjlab-microduck 0.1.0 @ 8d0dbc94c - started:
2026-10-14T12:55:17Z
Reproduce
Same code, same uv.lock, same command, same seed. Training it again yields a comparable policy, not the same weights: GPU reinforcement learning is not bit-reproducible across machines.
git clone https://github.com/pollen-robotics/microduck-challenges
cd microduck-challenges
git checkout 3cc03f144
uv sync
uv run train Mjlab-RollerSprint2m-MicroDuck --env.scene.num-envs 2048 --agent.max-iterations 100000 --agent.seed 6969 --agent.logger tensorboard --agent.run-name run-1014-1454
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