Abstract
Robust locomotion is one of the most fundamental requirements for autonomous mobile robots. With the widespread deployment of robots in factories, warehouses, and homes, it is tempting to think that locomotion is a solved problem. However for certain robot morphologies (e.g. humanoids) and environmental conditions (e.g. narrow passages), significant challenges remain. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot (sim-to-real). It then introduces Adaptive Planner ParameterLearning as a way of leveraging human input (learning from demonstration) towards making existing robot motion planners more robust, without losing their safety properties. Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot, and Adaptive Planner Parameter Learning has led to efficient learning of robust navigation policies in highly constrained spaces. Pre-read Materials can be accessed here.
Bio
Dr. Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Director of Texas Robotics, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone's research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, and robotics. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, ACM Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone co-founded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as Executive Director of Sony AI America.