Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
Xiaomeng Xu, Yifan Hou, Zeyi Liu, Shuran Song
Abstract
We address key challenges in Dataset Aggregation (DAgger) for real-world contact- rich manipulation: how to collect informative human correction data and how to effectively update policies with this new data. We introduce Compliant Residual DAgger (CR-DAgger), which contains two novel components: 1) a Compliant Intervention Interface that leverages compliance control, allowing humans to pro- vide gentle, accurate delta action corrections without interrupting the ongoing robot policy execution; and 2) a Compliant Residual Policy formulation that learns from human corrections while incorporating force feedback and force control. Our system significantly enhances performance on precise contact-rich manipu- lation tasks using minimal correction data, improving base policy success rates by over 60% on two challenging tasks (book flipping and belt assembly) while outperforming both retraining-from-scratch and finetuning approaches. Through extensive real-world experiments, we provide practical guidance for implementing effective DAgger in real-world robot learning tasks.
BibTeX
@inproceedings{
xu2025compliant,
title={Compliant Residual {DA}gger: Improving Real-World Contact-Rich Manipulation with Human Corrections},
author={Xiaomeng Xu and Yifan Hou and Zeyi Liu and Shuran Song},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=cjcm5LYVWm}
}