Hierarchical robot learning for physical collaboration between humans and robots

Zhen Deng, Jinpeng Mi, Dong Han, Rui Huang, Xiaofeng Xiong, Jianwei Zhang

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Human-in-the-loop robot learning is an important ability for robotics in human-robot collaborative (HRC) tasks. The research of interactive learning mainly focuses on robot learning with human cognitive interaction. However, robot learning with human physical interaction remains a challenging problem, due to the stochastic of human control. In this paper, we present a hierarchical robot learning approach that includes two learning hierarchies for HRC tasks. High-level motion learning is to learn the motion policy for objects which used as the shared plan of robot and human. In low-level interactive learning, human action is first predicted by an Extend Kalman Filter (EKF) algorithm. Q-learning with function approximation is applied to select the optimal robot action with the guidance of the predicted human action. Finally, the proposed learning approach is validated on a UR5 robot. The results of our experiments show the presented learning approach enables the robot to adaptively coordinate with a human and produce an active contribution to the HRC tasks.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Number of pages6
PublisherIEEE
Publication date23. Mar 2018
ISBN (Print)978-1-5386-3743-2
ISBN (Electronic)978-1-5386-3742-5, 978-1-5386-3741-8
DOIs
Publication statusPublished - 23. Mar 2018
Externally publishedYes

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