IEEE ICMA 2018 Conference

Plenary Talk II

Optimized Assistive Human-robot Interaction Using Reinforcement Learning

F. L. Lewis, Professor
National Academy of Inventors
Fellow IEEE, Fellow InstMC, Fellow IFAC, Fellow AAAS
Moncrief-O’Donnell Endowed Chair and Head, Advanced Controls & Sensors Group
UTA Research Institute (UTARI), The University of Texas at Arlington, USA
Qian Ren Thousand Talents Consulting Professor, Northeastern University, Shenyang, China
E-mail: lewis@uta.edu


Co-robotics involves humans and robots working together safely in the same shared space as a team. This motivates physical Human-Robot Interaction (HRI) systems that adapt to different humans and have guaranteed robustness and stability properties. For modern interactive HRI systems to be capable of performing a wide range of tasks successfully, it is required to include the effects of both the robot dynamics and the human operator dynamics. In this talk we propose three adaptive HRI control systems that assist the human operator to perform a given task with minimum human workload demands and improved overall human-robot system performance.
Human performance neuropsychological and human factors studies have shown that in coordinated motion with a robot, human learning has two components. The operator learns a robot-specific inverse dynamics model to compensate for the nonlinearities of the robot, and simultaneously learns a feedback control component that is specific to the successful performance of the task. These foundations can be incorporated in the design of HRI control systems that include the effects of both the robot dynamics and the human dynamics by using a 2-loop design procedure.
In this talk, we develop an adaptive HRI control structure consisting of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to most existing neural network and adaptive impedance based control methods, no information of the task performance (e.g. specifically no reference trajectory information) is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the best parameters of the prescribed robot impedance model to adjust the robot’s dynamics to the operator’s skills to effectively perform a given task. The outer loop includes the human operator dynamics and all the task performance details. Given the inner-loop neuro-adaptive robot controller, three different outer loop designs are given for robot-assisted task performance. Experimental results on a PR2 robot demonstrate the effectiveness of this approach in using the robot to improve the human’s performance of a motion task.

Prof. Biosketch F.L. Lewis is a member of National Academy of Inventors. Fellow IEEE, Fellow IFAC, Fellow AAAS, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer. UTA Distinguished Scholar Professor, UTA Distinguished Teaching Professor, and Moncrief-O’Donnell Chair at The University of Texas at Arlington Research Institute. Qian Ren Thousand Talents Consulting Professor, Northeastern University, Shenyang, China. Foreign Expert Scholar, Huazhong University of Science and Technology. IEEE Control Systems Society Distinguished Lecturer. Bachelor's Degree in Physics/EE and MSEE at Rice University, MS in Aeronautical Engineering at Univ. W. Florida, Ph.D. at Ga. Tech. He works in feedback control, reinforcement learning, intelligent systems, and distributed control systems. He is author of 6 U.S. patents, 363 journal papers, 418 conference papers, 20 books, 48 chapters, and 26 journal special issues. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, U.K. Inst. Measurement & Control Honeywell Field Engineering Medal 2009. Received IEEE Computational Intelligence Society Neural Networks Pioneer Award 2012 and AIAA Intelligent Systems Award 2016. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the Year by Ft. Worth IEEE Section. Listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Dean’s Excellence in Doctoral Mentoring Award. Elected to UTA Academy of Distinguished Teachers 2012. Texas Regents Outstanding Teaching Award 2013.

The Robotics Society of Japan Kagawa University Kagawa University The Japan Society of Mechanical Engineers Japan Society for Precision Engineering The Society of Instrument and Control Engineers Harbin Engineering University University of Electro-Communications University of Electronic Science and Technology of China Changchun University of Science and Technology