极值寻求控制的触觉插入振动
Extremum Seeking Controlled Wiggling for Tactile Insertion
AI总结：
本文提出了一种基于触觉反馈的极值寻求控制方法，用于提高机器人插入任务的鲁棒性和成功率。
摘要：
当人类执行复杂插入任务，如将杯子推入柜子、路由电缆或将钥匙插入锁中时，他们会摇动物体并通过触觉反馈调整过程。类似的人工机器人方法尚未开发。我们研究了一种极值寻求控制律，通过摇动末端执行器姿态来最大化插入深度，同时最小化由GelSight Mini传感器测量的应变。评估在四个具有复杂几何形状的钥匙和五个具有基本几何形状的装配任务上进行。在钥匙上，算法在120次试验中达到71%的成功率，在240次试验中达到84%的成功率，在40次初始化视觉试验中达到75%的成功率。它显著优于基线优化器CMA-ES，该优化器用随机采样代替摇动。在最先进装配基准测试中，它在50次视觉初始化试验中达到98%的成功率。该基准的最相似基线，训练于物体上，达到86%。这些结果，无需接触建模或学习，表明基于触觉反馈的闭环摇动是机器人插入的稳健范式。
英文摘要：
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.
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。
