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license: apache-2.0
tags:
- robotics
- reinforcement-learning
- discodemo
- franka
---
# DiscoDemo-Stage1_RL-FMB_Round-alpha0
The **data generator** of [DiscoDemo](https://davian-robotics.github.io/DiscoDemo/) for the **FMB-Round** task (insert a cylindrical peg into its hole on the FMB board): a state-based
reinforcement-learning policy trained in simulation — the P-RFCL baseline generator (α = 0: the same reverse-curriculum RL without the diversity reward).
Rolling it out generates demonstrations such as those in the Stage2 dataset below.
| Stage | Repository |
|---|---|
| Generator (RL policy) | [`DAVIAN-Robotics/DiscoDemo-Stage1_RL-FMB_Round-alpha0`](https://huggingface.co/DAVIAN-Robotics/DiscoDemo-Stage1_RL-FMB_Round-alpha0) (this repository) |
| Generated dataset | [`DAVIAN-Robotics/DiscoDemo-Stage2_GenData-FMB_Round-alpha0`](https://huggingface.co/datasets/DAVIAN-Robotics/DiscoDemo-Stage2_GenData-FMB_Round-alpha0) |
| Imitation policy (π0.5) | [`DAVIAN-Robotics/DiscoDemo-Stage3_SFT-FMB_Round-alpha0`](https://huggingface.co/DAVIAN-Robotics/DiscoDemo-Stage3_SFT-FMB_Round-alpha0) |
## Files
| File | Content |
|---|---|
| `actor.pt` | Actor network weights (`network_state_dict`). Optimizer state is not included. |
| `normalizer.pt` | Observation bounds used to normalize the policy input. |
| `config.yaml` | Full training configuration (environment, curriculum, agent). |
Only what is needed to roll the policy out is released; the critic and other training-only state are omitted.
Environment steps at this checkpoint: 50.0 M.
## Usage
Loading requires the DiscoDemo training code: https://github.com/DAVIAN-Robotics/DiscoDemo.
## Citation
```bibtex
@article{park2026discodemo,
title = {DiscoDemo: Discovering Efficient and Diverse Robot Demonstrations for Imitation Learning},
author = {Park, Minho and Kim, Kinam and Kim, Donghu and Lee, Byungkun and Hwang, Dongyoon and Shin, Yongjae and Hyung, Junha and Lee, Hojoon and Choo, Jaegul},
journal = {arXiv preprint},
year = {2026}
}
```
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