---
tags:
- microduck
- robotics
- reinforcement-learning
- onnx
- microduck-slot:walk
library_name: microduck
pipeline_tag: robotics
---

# roller-sprint-2m-reference

Pollen's reference for the Microduck Arena's 2 m Roller Sprint: the roller_sprint_2m challenge of microduck-challenges trained unchanged with its recipe (4096 envs, 3000 iterations, seed 1).

A **perpetual** policy for the [microduck](https://github.com/pollen-robotics/microduck) (61-D observation, 14 actions, 50 Hz, on rollers). Runs until told otherwise — a gait for the `walk` slot.

## Run it on a robot

```bash
sudo robotctl policy load walk pollen-robotics/microduck-roller-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-RollerSprint2m-MicroDuck`
- **repo**: `https://github.com/pollen-robotics/microduck-challenges.git`
- **branch**: `main`
- **commit**: `611b6453c`
- **checkpoint**: `2999`
- **seed**: `1`
- **base**: `mjlab-microduck 0.1.0 @ 981a279c6`
- **started**: `2026-09-26T04:21:50Z`

## 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 611b6453c
uv sync
uv run train Mjlab-RollerSprint2m-MicroDuck --env.scene.num-envs 4096 --agent.max-iterations 3000 --agent.seed 1 --agent.logger tensorboard --agent.run-name reference
```