Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
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.
Cite
Text
Xu et al. "Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections." Advances in Neural Information Processing Systems, 2025.Markdown
[Xu et al. "Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/xu2025neurips-compliant/)BibTeX
@inproceedings{xu2025neurips-compliant,
title = {{Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections}},
author = {Xu, Xiaomeng and Hou, Yifan and Liu, Zeyi and Song, Shuran},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/xu2025neurips-compliant/}
}