This repository contains the official trained policies for The files are organized by task environment and training seed: The play.py script in the JoSE repository downloads a pretrained model and executes it in the corresponding environment, allowing agent behaviour to be visualized. The script is called from the repo root as follows: Seeds vary by hand and task. See the HuggingFace model directory to see which seeds are available for each task-hand combination.
Open-weight model · Reinforcement learning
DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0
by DAVIAN Robotics DAVIAN-Robotics/DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0
DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0 is an open-weight model for reinforcement learning from DAVIAN Robotics, released under Apache License 2.0. Its published files total 1.1 MB. It draws 21 downloads a month.
The data generator of DiscoDemo for the FMB-SqCircle task (insert a two-pronged (cylinder and square) 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…
Parameters—
Context—
Weights1.1 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads21
Model Card
By DAVIAN Robotics, published under apache-2.0, revision fdcb123dd073.
The data generator of DiscoDemo for the FMB-SqCircle task (insert a two-pronged (cylinder and square) 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. Only what is needed to roll the policy out is released; the critic and other training-only state are omitted. Loading requires the DiscoDemo training code: https://github.com/DAVIAN-Robotics/DiscoDemo.
Read DAVIAN Robotics's full model card
The data generator of DiscoDemo for the FMB-SqCircle task (insert a two-pronged (cylinder and square) 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_SqCircle-alpha0 (this repository) |
| Generated dataset | DAVIAN-Robotics/DiscoDemo-Stage2_GenData-FMB_SqCircle-alpha0 |
| Imitation policy (π0.5) | DAVIAN-Robotics/DiscoDemo-Stage3_SFT-FMB_SqCircle-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: 20.0 M.
Usage
Loading requires the DiscoDemo training code: https://github.com/DAVIAN-Robotics/DiscoDemo.
Citation
@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}
}
Identity and Version
- Repository
- DAVIAN-Robotics/DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0
- Publisher
- DAVIAN Robotics
- Task
- Reinforcement learning
- Modality
- Control
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- fdcb123dd0731cc5ecad5e58f96b4faa09bc9594
- First published
- 2026-10-07
- Last updated
- 2026-10-13
Files and Weights
5 files, 1.1 MB in total. The weights are 2 files totalling 1.1 MB in pt.
Weights2 files · 1.1 MB
Configuration1 file · 3.0 KB
Documentation1 file · 2.1 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| actor.pt | Weights | 1.1 MB | 053efac08f89 |
| normalizer.pt | Weights | 2.4 KB | 7b237a781e41 |
| config.yaml | Configuration | 3.0 KB | — |
| README.md | Documentation | 2.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 1.1 MB
Download from DAVIAN Robotics
Released by DAVIAN Robotics through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.1 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0
Can I use DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0 commercially?
Yes. DiscoDemo-Stage1_RL-FMB_SqCircle-alpha0 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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The data generator of 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. Only what is needed to roll the policy out is released; the critic and other training-only state are omitted. Loading requires the DiscoDemo training code: https://github.com/DAVIAN-Robotics/DiscoDemo.
The data generator of DiscoDemo for the FMB-SqCircle task (insert a two-pronged (cylinder and square) peg into its hole on the FMB board): a state-based reinforcement-learning policy trained in simulation — the DiscoDemo generator (diversity weight α = 0.3). Rolling it out generates demonstrations such as those in the Stage2 dataset below. Only what is needed to roll the policy out is released; the critic and other training-only state are omitted. The actor is conditioned on a skill vector z sampled once per episode from a standard normal distribution. Loading requires the DiscoDemo training code: https://github.com/DAVIAN-Robotics/DiscoDemo.