Grasp stiffness control in robotic hands through coordinated optimization of pose and joint stiffness

Ruiz Garate, Virginia; Pozzi, Maria; Prattichizzo, Domenico; Tsagarakis, Nikos; Ajoudani, Arash

Abstract

This letter presents a novel controller for robotic hands, which regulates the grasp stiffness by manipulating the pose and the finger joint stiffness of hands with multiple degrees of freedom while guaranteeing grasp stability. The proposed approach is inspired by the observations of human motor behavior, which reveal a coordinated pattern of stiffening among the hand fingers, along with a predictive selection of the hand pose to achieve a reliable grasp. The first adjusts the magnitude of the grasp stiffness, while the latter manipulates its overall geometry (shape). The realization of a similar control approach in robotic hands can result in a reduction of the software complexity and also promote a novel mechanical design approach, in which the finger stiffness profiles of the hand are adjusted by only one active component. The proposed control is validated with a fully actuated Allegro Hand, while trying to achieve predefined grasp stiffness profiles or modifications of an initial one.

• Journal Article Type: Article
• Acceptance Date: Jun 29, 2018
• Online Publication Date: Jul 23, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Mar 10, 2021
• Journal: IEEE Robotics and Automation Letters
• Print ISSN: 2377-3766
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 4
• Pages: 3952-3959
• DOI: https://doi.org/10.1109/lra.2018.2858271
• Keywords: Artificial Intelligence; Computer Vision and Pattern Recognition; Computer Science Applications
• Public URL: https://uwe-repository.worktribe.com/output/7033318