T-TD3: A Reinforcement Learning Framework for Stable Grasping of Deformable Objects Using Tactile Prior

作者：Yanmin Zhou, Yiyang Jin, Ping Lu, Shuo Jiang, Zhipeng Wang, Bin He · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2025 · DOI：10.1109/tase.2024.3440047 · 被引用次数：20 · 研究领域：Computer Science

Human tactile perception enables rapid assessment of deformable objects and the application of appropriate force to prevent slip or excessive deformation. However, this task remains challenging for robots. To address this issue, we propose the T-TD3 algorithm, which utilizes a multi-scale fusion neural network (MSF-Net) for the fused perception of multi-scale features, including the tactile prior information obtained through preprocessing. Our approach decomposes the robot task of grasping deformable objects into three subtasks: slip detection, stable grasping evaluation, and minimum grasping force tracking. We develop a simulation environment called CR5GraspStable-Env using PyBullet and TACTO for the network training. Our work reports a success rate of 94.81% in the robot task of grasping deformable objects in real, demonstrating an excellent sim-to-real capability. Moreover, the proposed approach has the potential to be extended to other stable grasping tasks that utilize tactile perception. Note to Practitioners—Traditional grasping strategies in stable grasping tasks typically apply significant grasping force to prevent slip. However, excessive grasping force for deformable objects may lead to excessive deformation and damage. While humans can flexibly control grasping force through tactile perception, it poses a significant challenge for robots. To overcome this challenge, we propose a novel method for the robot to learn an improved stable grasping strategy by incorporat...