用触觉反馈控制机械臂微动，实现高成功率插入操作。
Extremum Seeking Controlled Wiggling for Tactile Insertion
- 通过极值搜索控制让末端执行器微动，最大化插入深度并减小应变。
- 在复杂钥匙任务中达71%~84%成功率，基本几何装配任务达98%。
- 无需接触建模或学习，适合无先验知识的插入场景。
人类在完成推杯入柜、穿线缆、插钥匙等复杂插入任务时，会通过微动物体并依赖触觉反馈进行调整。现有机器人方法尚未实现类似策略。本文研究一种极值搜索控制律，通过微动末端执行器姿态来最大化插入深度，同时最小化由GelSight Mini传感器测得的应变。在四个具有复杂几何结构的钥匙和五个基本几何结构的装配任务上进行了评估。在钥匙任务中，采用6自由度扰动时，120次试验成功率达71%；1自由度扰动下，240次试验成功率达84%；视觉初始化时，40次试验成功率为75%。该方法显著优于使用随机采样的基线优化器CMA-ES。在最先进的装配基准测试中，50次视觉初始化试验成功率达98%，而最相似的基线（在同类物体上训练）仅达86%。这些成果在不依赖接触建模或学习的情况下达成，表明基于触觉反馈的闭环微动是一种鲁棒的机器人插入范式。
原文摘要 · Abstract (English)
When humans perform complex insertion tasks such as pushing a cup into a cupboard, routing a cable, or putting a key in a lock, they wiggle the object and adapt the process through tactile feedback. A similar robotic approach has not been developed. We study an extremum seeking control law that wiggles end effector pose to maximize insertion depth while minimizing strain measured by a GelSight Mini sensor. Evaluation is conducted on four keys featuring complex geometry and five assembly tasks featuring basic geometry. On keys, the algorithm achieves 71% success rate over 120 trials with 6-DOF perturbations, 84% over 240 trials with 1-DOF perturbations, and 75% over 40 trials initialized with vision. It significantly outperforms a baseline optimizer, CMA-ES, that replaces wiggling with random sampling. When tested on a state-of-the-art assembly benchmark featuring basic geometry, it achieves 98% over 50 vision-initialized trials. The benchmark's most similar baseline, which was trained on the objects, achieved 86%. These results, realized without contact modeling or learning, show that closed loop wiggling based on tactile feedback is a robust paradigm for robotic insertion.
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