2024/11/13 by Yueming Hu, Mengde Li, Hu, Yueming +9 · 1 citation
Health Professions · Neuroscience · Psychology · #Educational Games and Gamification #FOS: Computer and information sciences #Neuroscience, Education and Cognitive Function #Robotics (cs.RO) #Sports and Physical Education Research
paper · pdf · doi:10.48550/arxiv.2411.08499
openalex publication_date 2024/11/13 · openalex created_date 2024/11/16 · openalex updated_date 2026/07/28
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.