Estimating Tactile Data for Adaptive Grasping of Novel Objects

TL;DR

This paper introduces an adaptive grasping approach that simulates tactile data to evaluate and improve grasp stability on novel objects, achieving an 88% success rate in experiments.

Contribution

It presents a novel method for grasp adaptation using simulated tactile data to enhance stability on unseen objects.

Findings

Achieved 88% success rate on YCB objects

Improved grasp stability through tactile data simulation

System can plan, apply, and adapt grasps on novel objects

Abstract

We present an adaptive grasping method that finds stable grasps on novel objects. The main contributions of this paper is in the computation of the probability of success of grasps in the vicinity of an already applied grasp. Our method performs grasp adaptions by simulating tactile data for grasps in the vicinity of the current grasp. The simulated data is used to evaluate hypothetical grasps and thereby guide us toward better grasps. We demonstrate the applicability of our method by constructing a system that can plan, apply and adapt grasps on novel objects. Experiments are conducted on objects from the YCB object set and the success rate of our method is 88%. Our experiments show that the application of our grasp adaption method improves grasp stability significantly.