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. In this talk, I will first discuss our recent work on developing efficient computational tools for simulating and controlling human movements. By learning a differentiable kinematic constraints from the real world human motion data, we enable existing multi-body physics engines to simulate more humanlike motion. In a similar vein, we learn task-agnostic boundary conditions and energy functions from anatomically realistic neuromuscular models, effectively defining a new action space better reflecting the physiological constraints of the human body. The second part of the talk will focus on two different yet highly relevant problems: how to teach robots to move like humans and how to teach robots to interact with humans. While Computer Animation research has shown that it is possible to teach a virtual human to mimic real athletes' movements, the current techniques still struggle to reliably transfer a basic locomotion control policy to robot hardware in the real world. We developed a series of sim-to-real transfer methods to address the intertwined issue of system identification and policy learning for challenging locomotion tasks. Finally, I will showcase our effort on teaching robot to physically interact with humans in the scenarios of robot-assisted dressing and walking assistance.
C. Karen Liu is an associate professor in the Department of Computer Science at Stanford University. 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.