触覚フィードバックによる極値探索制御ウィグリング挿入
Extremum Seeking Controlled Wiggling for Tactile Insertion
GelSight Miniセンサのひずみを最小化しながら挿入深さを最大化するようエンドエフェクタをウィグリングさせる極値探索制御則を提案し、鍵や組立タスクで高い成功率を達成した。
著者: Levi Burner, Pavan Mantripragada, Gabriele M. Caddeo, Lorenzo Natale, Cornelia Fermüller, Yiannis Aloimonos
分類: cs.RO
原文アブストラクト
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.
