Extremum Seeking Controlled Wiggling for Tactile Insertion

TL;DR

This paper introduces a tactile feedback-based extremum seeking control method that wiggling the robot's end effector to improve insertion success rates in complex tasks, outperforming baseline algorithms without contact modeling or learning.

Contribution

The paper presents a novel extremum seeking control law for robotic insertion tasks that uses tactile feedback to adaptively wiggle the end effector, demonstrating superior success rates over traditional methods.

Findings

Achieves up to 98% success on a benchmark with basic geometry.

Outperforms CMA-ES baseline in success rate.

Effective without contact modeling or learning.

Abstract

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.