力覚対応残差DAggerと軌道編集によるインピーダンス制御下の精密挿入
Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
接触を伴う精密挿入タスクのための、力覚を考慮した人間参加型模倣学習フレームワークを提案。軌道編集による残差方策学習と力の不一致に基づく介入トリガーで、共変量シフトを抑えつつ専門家の常時監視を不要にした。
詳しい要約
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著者: Yiou Huang, Ning Ma, Weichu Zhao, Zinuo Liu, Jun Sun, Qiufeng Wang, Yaran Chen
分類: cs.RO
原文アブストラクト
Imitation learning (IL) has shown strong potential for contact-rich precision insertion tasks. However, its practical deployment is often hindered by covariate shift and the need for continuous expert monitoring to recover from failures during execution. In this paper, we propose Trajectory Editing Residual Dataset Aggregation (TER-DAgger), a scalable and force-aware human-in-the-loop imitation learning framework that mitigates covariate shift by learning residual policies through optimization-based trajectory editing. This approach smoothly fuses policy rollouts with human corrective trajectories, providing consistent and stable supervision. Second, we introduce a force-aware failure anticipation mechanism that triggers human intervention only when discrepancies arise between predicted and measured end-effector forces, significantly reducing the requirement for continuous expert monitoring. Third, all learned policies are executed within a Cartesian impedance control framework, ensuring compliant and safe behavior during contact-rich interactions. Extensive experiments in both simulation and real-world precision insertion tasks show that TER-DAgger improves the average success rate by over 37\% compared to behavior cloning, human-guided correction, retraining, and fine-tuning baselines, demonstrating its effectiveness in mitigating covariate shift and enabling scalable deployment in contact-rich manipulation.
