A robot learning framework based on adaptive admittance control and generalizable motion modeling with neural network controller

Wang, Ning; Chen, Chuize; Yang, Chenguang

Abstract

© 2019 Elsevier B.V. Robot learning from demonstration (LfD) enables robots to be fast programmed. This paper presents a novel LfD framework involving a teaching phase, a learning phase and a reproduction phase, and proposes methods in each of these phases to guarantee the overall system performance. An adaptive admittance controller is developed to take into account the unknown human dynamics so that the human tutor can smoothly move the robot around in the teaching phase. The task model in this controller is formulated by the Gaussian mixture regression to extract the human-related motion characteristics. In the learning and reproduction phases, the dynamic movement primitive is employed to model a robotic motion that is generalizable. A neural network-based controller is designed for the robot to track the trajectories generated from the motion model, and a radial basis function neural network is used to compensate for the effect caused by the dynamic environments. Experiments have been performed using a Baxter robot and the results have confirmed the validity of the proposed robot learning framework.

• Journal Article Type: Article
• Acceptance Date: Apr 30, 2019
• Online Publication Date: Oct 21, 2019
• Publication Date: May 21, 2020
• Deposit Date: Nov 18, 2019
• Publicly Available Date: Oct 22, 2020
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 390
• Pages: 260-267
• DOI: https://doi.org/10.1016/j.neucom.2019.04.100
• Keywords: Cognitive Neuroscience; Artificial Intelligence; Computer Science Applications; Robot learning; Adaptive admittance control; Motion generalization; Neural network
• Public URL: https://uwe-repository.worktribe.com/output/4680130
• Publisher URL: https://doi.org/10.1016/j.neucom.2019.04.100