HAI Seminar: Deep Reinforcement Learning for Physical Human-Robot Interaction, Karen Liu, Associate Professor, Computer Science Department, Stanford University

HAI Seminar<br><br>Title: Deep Reinforcement Learning for Physical Human-Robot Interaction<br>Speaker: Karen Liu, Associate Professor, Computer Science Department, Stanford University<br>Date: May 26<br>Time: 10:00am<br>RSVP Here: https://stanford.zoom.us/webinar/register/WN_wEPCCPvpQgu_qsh2F_T6-Q<br><... realistic virtual humans has traditionally been considered a research problem in Computer Animation&nbsp;primarily for entertainment applications. With the recent breakthrough in collaborative robots and deep&nbsp;reinforcement learning, accurately modeling human movements and behaviors has become a common&nbsp;challenge also faced by researchers in robotics and artificial intelligence. For example, mobile robots and&nbsp;autonomous vehicles can benefit from training in environments populated with ambulating humans and&nbsp;learning to avoid colliding with them. Healthcare robotics, on the other hand, need to embrace physical&nbsp;contacts and learn to utilize them for enabling human’s activities of daily living.&nbsp;An immediate concern in&nbsp;developing such an autonomous and powered robotic device is the safety of human users during the early&nbsp;development phase when the control policies are still largely suboptimal.&nbsp;Learning from physically simulated&nbsp;humans and environments presents a promising alternative which enables robots to&nbsp;safely make and learn&nbsp;from mistakes without putting real people at risk.&nbsp;However, deploying such policies to interact with people in&nbsp;the real world adds additional complexity to the already challenging sim-to-real transfer problem.&nbsp;<br><p>In this talk,&nbsp;Karen will present current progress on solving the problem of sim-to-real transfer with humans in the&nbsp;environment, actively interacting with the robots through physical contacts. She will tackle the problem from two&nbsp;fronts: developing more relevant human models to facilitate robot learning and developing human-aware robot&nbsp;perception and control policies. As an example of contextualizing her research effort, we&nbsp;develop a mobile&nbsp;manipulator to put clothes on people with physical impairments, enabling them to carry out day-to-day tasks&nbsp;and maintain independence.&nbsp;</p><br><br>Bio:&nbsp;<br>C. Karen Liu is an associate professor in the Computer Science Department at Stanford University. Prior to joining Stanford, Liu was a faculty member at the School of Interactive Computing at Georgia Tech. She received her Ph.D. degree in Computer Science from the University of Washington. Liu's research interests are in computer graphics and robotics, including physics-based animation, character animation, optimal control, reinforcement learning, and computational biomechanics. She developed computational approaches to modeling realistic and natural human movements, learning complex control policies for humanoids and assistive robots, and advancing fundamental numerical simulation and optimal control algorithms. The algorithms and software developed in her lab have fostered interdisciplinary collaboration with researchers in robotics, computer graphics, mechanical engineering, biomechanics, neuroscience, and biology. Liu received a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and was named Young Innovators Under 35 by Technology Review. In 2012, Liu received the ACM SIGGRAPH Significant New Researcher Award for her contribution in the field of computer graphics.

Wednesday, May 26, 2021 - 10:00am to 11:00am