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ICS, CNE, HCR Research Seminar for Doctoral Candidates on "Sample-Efficient Reinforcement Learning for Real-World Robot Control"

04.02.2019


ICS, CNE, HCR Research Seminar for Doctoral Candidates on "Sample-Efficient Reinforcement Learning for Real-World Robot Control" by Prof. Takamitsu Matsubara, Nara Institute of Science and Technology (NAIST)

Abstract: Reinforcement learning (RL) has been explored in a broad range of robot control scenarios, however, its application to real-world robots still remains difficult since a prohibitively long-time experiment for collecting sufficient data samples is often required. Therefore, developing sample-efficient RL algorithms is of primary importance. In this talk, I introduce some of the sample-efficient RL algorithms we developed recently and present their applications to robotic cloth manipulation and real-boat autopilot and so on.

Bio: Associate Professor and Head of Robot Learning Laboratory, Nara Institute of Science and Technology, Nara, Japan. He received his Ph.D. in information science from the Nara Institute of Science and Technology, Nara, Japan, in 2007. From 2005 to 2007, he was a research fellow (DC1) of the Japan Society for the Promotion of Science. From 2013 to 2014, he is a visiting researcher of the Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands. He is currently an associate professor and the head of robot learning laboratory at the Nara Institute of Science and Technology. He is also a visiting researcher at the ATR Computational Neuroscience Laboratories, Kyoto, Japan, and the National Institute of Advanced Industrial Science (AIST), and a technical advisor at OMRON SINIC X Corporation, Tokyo, Japan. His research interests are machine learning and control theory for robotics.

6 February 2019,16:30h, ICS Karlstraße 45, 6. Floor, room 6009

For more info please visit: http://web.ics.ei.tum.de/~ehrlich/ICS_seminar/index.htm