AUTHOR=Deng Zhifeng , Li Miao TITLE=Learning Optimal Fin-Ray Finger Design for Soft Grasping JOURNAL=Frontiers in Robotics and AI VOLUME=7 YEAR=2021 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2020.590076 DOI=10.3389/frobt.2020.590076 ISSN=2296-9144 ABSTRACT=

The development of soft hands is an important progress to empower robotic grasping with passive compliance while greatly decreasing the complexity of control. Despite the advances during the past decades, it is still not clear how to design optimal hands or fingers given the task requirements. In this paper, we propose a framework to learn the optimal design parameter for a fin-ray finger in order to achieve stable grasping. First, the pseudo-kinematics of the soft finger is learned in simulation. Second, the task constraints are encoded as a combination of desired grasping force and the empirical grasping quality function in terms of winding number. Finally, the effectiveness of the proposed approach is validated with experiments in simulation and using real-world examples as well.