Instructions to use TheSauceman/microduck-electric-slide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Microduck
How to use TheSauceman/microduck-electric-slide with Microduck:
# Replace SLOT with the slot specified in the model card (walk, stand, sitstand, ground_pick, kick_left, kick_right, roulade). sudo robotctl policy load SLOT TheSauceman/microduck-electric-slide
- Notebooks
- Google Colab
- Kaggle
electric-slide
MicroDuck dances the Electric Slide and sings along to Achy Breaky Heart
A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot.
Run it on a robot
sudo robotctl policy load <slot> TheSauceman/microduck-electric-slide
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-GotATalent-MicroDuck - repo:
https://github.com/SteveTarter/microduck-challenges - branch:
claude/project-thread-h4e0o0 - commit:
238700562 - checkpoint:
5999 - seed:
1 - base:
mjlab-microduck 0.1.0 @ 8d0dbc94c - started:
2026-10-19T21:55:47Z
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/SteveTarter/microduck-challenges
cd microduck-challenges
git checkout 238700562
uv sync
uv run train Mjlab-GotATalent-MicroDuck --env.scene.num-envs 2048 --agent.max-iterations 6000 --agent.seed 1 --agent.logger tensorboard --agent.run-name dance2
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