Invited talk: Karen Liu "Deep Reinforcement Learning for Physical Human-Robot Interaction"

Creating realistic virtual humans has traditionally been considered a research problem in Computer Animation primarily for entertainment applications. With the recent breakthrough in collaborative robots and deep reinforcement learning, accurately modeling human movements and behaviors has become a common challenge also faced by researchers in robotics and artificial intelligence. For example, mobile robots and autonomous vehicles can benefit from training in environments populated with ambulating humans and learning to avoid colliding with them. Healthcare robotics, on the other hand, need to embrace physical contacts and learn to utilize them for enabling human’s activities of daily living. An immediate concern in developing such an autonomous and powered robotic device is the safety of human users during the early development phase when the control policies are still largely suboptimal. Learning from physically simulated humans and environments presents a promising alternative which enables robots to safely make and learn from mistakes without putting real people at risk. However, deploying such policies to interact with people in the real world adds additional complexity to the already challenging sim-to-real transfer problem. In this talk, I will present our current progress on solving the problem of sim-to-real transfer with humans in the environment, actively interacting with the robots through physical contacts. We tackle the problem from two fronts: developing more relevant human models to facilitate robot learning and developing human-aware robot perception and control policies. As an example of contextualizing our research effort, we develop a mobile manipulator to put clothes on people with physical impairments, enabling them to carry out day-to-day tasks and maintain independence.