Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill

TL;DR

This paper presents a tactile-based adaptive grasping policy that leverages human demonstrations to improve robustness against disturbances, demonstrating strong generalization across diverse objects and dynamic tasks.

Contribution

Introduces a novel tactile sensing and human-demonstration-based adaptive grasping method that enhances robustness and generalization in robotic grasping tasks.

Findings

Effective in resisting external disturbances

Generalizes well to various object sizes and shapes

Performs reliably in dynamic force interactions

Abstract

Robust grasping represents an essential task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects. Previous research has extensively combined tactile sensing with grasping, primarily relying on rule-based approaches, frequently neglecting post-grasping difficulties such as external disruptions or inherent uncertainties of the object's physics and geometry. To address these limitations, this paper introduces an human-demonstration-based adaptive grasping policy base on tactile, which aims to achieve robust gripping while resisting disturbances to maintain grasp stability. Our trained model generalizes to daily objects with seven different sizes, shapes, and textures. Experimental results demonstrate that our method performs well in dynamic and force interaction tasks and exhibits excellent generalization ability.