# Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill

arXiv: https://arxiv.org/abs/2411.08499 · HTML: https://mlsanity.com/paper/2411.08499/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2024.

## Built on by (most cited first, 2 of 2)

- [Surformer v1: Transformer-Based Surface Classification Using Tactile and Vision Features](https://mlsanity.com/paper/2508.06566/) (2025)
- [MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping](https://mlsanity.com/paper/2602.06504/) (2026)

## Abstract (arXiv)

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.