通过触觉反馈实现自适应抓取，提升机器人抓握稳定性。
Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill
- 基于人类示范学习触觉驱动的自适应抓取策略。
- 在7种不同尺寸、形状和材质物体上表现稳定，抗干扰能力强。
- 适合需要高鲁棒性抓取的工业与服务机器人场景。
稳健抓取是机器人领域的重要任务，需依赖触觉反馈和动态调整以应对复杂情况。以往研究多采用规则驱动方法，常忽略抓取后受外部扰动或物体物理与几何不确定性的影响。为此，本文提出一种基于人类示范的触觉自适应抓取策略，旨在提升抓取鲁棒性并抵抗干扰。所训练模型在7种不同尺寸、形状和纹理的日常物体上具有良好的泛化能力。实验表明，该方法在动态交互与力控任务中表现优异，具备出色的适应性。
原文摘要 · Abstract (English)
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.
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