BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Peter Stone (University of Texas at Austin) DTSTART:20200730T190000Z DTEND:20200730T203000Z DTSTAMP:20260423T021040Z UID:IASML/15 DESCRIPTION:Title: E fficient Robot Skill Learning via Grounded Simulation Learning\, Imitation Learning from Observation\, and Off-Policy Reinforcement Learning\nby Peter Stone (University of Texas at Austin) as part of IAS Seminar Series on Theoretical Machine Learning\n\n\nAbstract\nFor autonomous robots to o perate in the open\, dynamically changing world\, they will need to be abl e to learn a robust set of skills from relatively little experience. 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. It then intro duces two new algorithms for imitation learning from observation that enab le a robot to mimic demonstrated skills from state-only trajectories\, wit hout any knowledge of the actions selected by the demonstrator. Connection s to theoretical advances in off-policy reinforcement learning will be hig hlighted throughout.\n\nGrounded Simulation Learning has led to the fastes t known stable walk on a widely used humanoid robot\, and imitation learni ng from observation opens the possibility of robots learning from the vast trove of videos available online.\n LOCATION:https://researchseminars.org/talk/IASML/15/ END:VEVENT END:VCALENDAR