合规残差DAgger：利用人类纠正改进现实中的接触密集操作
Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
AI总结：
CR-DAgger通过合规干预接口和残差策略提升现实接触密集操作任务的性能，以64%的提升率改进基线策略。
摘要：
我们针对现实世界接触密集操作中数据集聚合（DAgger）面临的关键挑战：如何收集有信息的人类纠正数据以及如何有效利用这些新数据更新策略。我们引入了合规残差DAgger（CR-DAgger），其包含两个新颖的组件：1）一个合规干预接口，利用合规控制，允许人类提供温和且准确的delta动作纠正，而不会中断正在进行的机器人策略执行；2）一个合规残差策略公式，通过人类纠正学习的同时结合力反馈和力控制。我们的系统在使用最少的纠正数据的情况下显著提高了精确接触密集操作任务的性能，在四个具有挑战性的任务（翻书、皮带组装、电缆路由和齿轮插入）中，基线策略的成功率提高了64%，同时优于从头重新训练和微调方法。通过广泛的现实世界实验，我们提供了在现实机器人学习任务中实施有效DAgger的实用指导。结果视频可在：https://compliant-residual-dagger.github.io 上找到。
英文摘要：
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 provide 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 manipulation tasks using minimal correction data, improving base policy success rates by 64% on four challenging tasks (book flipping, belt assembly, cable routing, and gear insertion) 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. Result videos are available at: https://compliant-residual-dagger.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
