File size: 1,689 Bytes
6773224 62afcc4 6773224 3e59e80 6773224 3e59e80 6773224 3e59e80 6773224 3e59e80 6773224 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ---
tags:
- microduck
- robotics
- reinforcement-learning
- onnx
- microduck-slot:walk
library_name: microduck
pipeline_tag: robotics
---
# sprint-2m-reference
Pollen's reference for the Microduck Arena's 2 m Sprint: the sprint_2m challenge of microduck-challenges trained unchanged with its recipe (4096 envs, 3000 iterations, seed 1); checkpoint 1000, its fastest on the Arena.
A **perpetual** policy for the [microduck](https://github.com/pollen-robotics/microduck) (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait for the `walk` slot.
## Run it on a robot
```bash
sudo robotctl policy load walk pollen-robotics/microduck-sprint-2m-reference
```
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-Sprint2m-MicroDuck`
- **repo**: `https://github.com/pollen-robotics/microduck-challenges.git`
- **branch**: `heading-hold`
- **commit**: `7a668f11c`
- **checkpoint**: `1000`
- **seed**: `1`
- **base**: `mjlab-microduck 0.1.0 @ 981a279c6`
- **started**: `2026-09-28T15:04:10Z`
## 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.
```bash
git clone https://github.com/pollen-robotics/microduck-challenges.git
cd microduck-challenges
git checkout 7a668f11c
uv sync
uv run train Mjlab-Sprint2m-MicroDuck --env.scene.num-envs 4096 --agent.max-iterations 3000 --agent.seed 1 --agent.logger tensorboard --agent.run-name reference
```
|