Deep Reinforcement Learning for Robotics
Deep learning has enabled significant advances in supervised learning problems such as speech recognition and visual recognition. Reinforcement learning provides only a weaker supervisory signal, posing additional challenges in the form of temporal credit assignment and exploration. Nevertheless, deep reinforcement learning has already enabled learning to play Atari games from raw pixels (without access to the underlying game state) and learning certain types of visuomotor manipulation primitives. I will discuss major challenges for, as well as some preliminary promising results towards, making deep reinforcement learning applicable to real robotic problems.
Pieter Abbeel (Associate Professor, UC Berkeley EECS) works in machine learning and robotics, in particular research on making robots learn by watching 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. His awards include best paper awards at ICML and ICRA, Young Investigator Awards from AFOSR, ONR, Darpa and NSF, the Sloan Fellowship, the MIT TR35, the IEEE Robotics and Automation Society Early Career Award, and the Dick Volz Best U.S. Ph.D. Thesis in Robotics and Automation Award.