Invited Talk: Safe Reinforcement Learning for Robotics (Pieter Abbeel, UC Berkeley and OpenAI)

Abstract: Recent advances in deep reinforcement learning have enabled a wide range of capabilities, including learning to play Atari games, learning (simulated) locomotion, and learning (real robot) visuomotor skills. A key issue in the application to real robotics, however, is safety during learning. In this talk I will discuss approaches to make learning safer through incorporation of classical model-predictive control into learning and through safe adaptive transfer of skills from simulated to real environments.

Bio: Pieter Abbeel (Associate Professor, UC Berkeley EECS) works in machine learning and robotics, in particular his research is on making robots learn from people (apprenticeship learning) and how to make robots learn through their own trial and error (reinforcement learning). His robots have learned: advanced helicopter aerobatics, knot-tying, basic assembly, and organizing laundry. He has won various awards, including best paper awards at ICML and ICRA, the Sloan Fellowship, the Air Force Office of Scientific Research Young Investigator Program (AFOSR-YIP) award, the Office of Naval Research Young Investigator Program (ONR-YIP) award, the DARPA Young Faculty Award (DARPA-YFA), the National Science Foundation Faculty Early Career Development Program Award (NSF-CAREER), the Presidential Early Career Award for Scientists and Engineers (PECASE), the CRA-E Undergraduate Research Faculty Mentoring Award, the MIT TR35, the IEEE Robotics and Automation Society (RAS) Early Career Award, and the Dick Volz Best U.S. Ph.D. Thesis in Robotics and Automation Award.