Improving Grasp Stability with Rotation Measurement from Tactile Sensing

TL;DR

This paper introduces a model-based method using GelSight tactile sensors to detect rotational displacement during grasping, enabling a robot to adjust and achieve more stable grasps through a closed-loop regrasping system.

Contribution

The work presents a novel rotation detection algorithm from tactile data and integrates it into a regrasping framework to improve grasp stability.

Findings

Accurately detects rotational displacement using GelSight sensors.

Enhances grasp stability through closed-loop regrasping.

Validated on real datasets and online experiments.

Abstract

Rotational displacement about the grasping point is a common grasp failure when an object is grasped at a location away from its center of gravity. Tactile sensors with soft surfaces, such as GelSight sensors, can detect the rotation patterns on the contacting surfaces when the object rotates. In this work, we propose a model-based algorithm that detects those rotational patterns and measures rotational displacement using the GelSight sensor. We also integrate the rotation detection feedback into a closed-loop regrasping framework, which detects the rotational failure of grasp in an early stage and drives the robot to a stable grasp pose. We validate our proposed rotation detection algorithm and grasp-regrasp system on self-collected dataset and online experiments to show how our approach accurately detects the rotation and increases grasp stability.