Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration

Dai, Hengtai; Lu, Zhenyu; He, Mengyuan; Yang, Chenguang

Abstract

Learning from demonstration (LfD) has been developed and proved to be a promising method for transferring skill knowledge from human to robot. It is desired to have a demonstration device that can effectively map demonstrations to the robot's motion to compensate for the correspondence problem in LfD. Thus, we presented a novel design of a gripper-like exoskeleton for robotic grasping based on Learning from Demonstration. The exoskeleton collected the displacement of its grippers, position, and posture information in the demonstration. This paper first presented the mechatronic design of the exoskeleton and then described the experiment for data validation. We illustrated the preliminary functionality of the exoskeleton by reproducing the demonstration trajectory on the Franka Emika robot.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022
• Start Date: Sep 1, 2022
• End Date: Sep 3, 2022
• Acceptance Date: Aug 1, 2022
• Publication Date: Oct 10, 2022
• Deposit Date: Oct 25, 2022
• Publicly Available Date: Oct 11, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2022 27th International Conference on Automation and Computing (ICAC)
• ISBN: 978-1-6654-9808-1
• DOI: https://doi.org/10.1109/ICAC55051.2022.9911096
• Keywords: Exoskeleton, Robotics, Learning from demonstration, Robotic grasping
• Public URL: https://uwe-repository.worktribe.com/output/10104137
• Publisher URL: https://ieeexplore.ieee.org/document/9911096
• Related Public URLs: http://www.cacsuk.co.uk/index.php/icac2022; ; https://ieeexplore.ieee.org/xpl/conhome/9911058/proceeding