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license: apache-2.0
tags:
- robotics
- reinforcement-learning
- humanoid
---
# FADA Oracle PPO Checkpoints
Oracle PPO expert checkpoints for **FADA: Few-Shot Domain Adaptation via Dynamics Alignment for
Humanoid Control** (arXiv:2606.28476), used as the `--expert-checkpoint` input to the DAgger
distillation step of the FADA pipeline.
- Code: https://github.com/lecar-lab/FADA
- Paper: https://arxiv.org/abs/2606.28476
- Project page: https://lecar-lab.github.io/FADA-humanoid/
## Contents
- `t1/`: Booster T1 (23-DoF) oracle PPO training run, every intermediate checkpoint saved every
100 iterations (`model_00000.pt` ... `model_24999.pt`, 251 files).
- `g1/`: Unitree G1 (29-DoF) oracle PPO training run, every intermediate checkpoint saved every
100 iterations (`model_00000.pt` ... `model_24999.pt`, 251 files).
The full intermediate history is included, not just the final checkpoint: FADA's DAgger training
reward-samples 20 checkpoints from across the oracle run for its weak-policy ("suboptimal-data")
source, so `--expert-checkpoint` must point into a directory holding every intermediate
`model_*.pt`. See the code repository's README for the exact usage and how to opt out of that
source (`--suboptimal-data-ratio 0`), which only requires the single final checkpoint.
## Security note
A `.pt` checkpoint is executable content. The FADA codebase loads these with torch's restricted
`weights_only=True` reader, which raises rather than executing an untrusted pickle stream.
## Citation
```bibtex
@article{xie2026fada,
title = {FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control},
author = {Xie, Angchen and Sobanbabu, Nikhil and Shikhare, Ishayu and Wang, Alan
and Simchowitz, Max and Shi, Guanya},
journal = {arXiv preprint arXiv:2606.28476},
year = {2026},
doi = {10.48550/arXiv.2606.28476}
}
```
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