5th Workshop on
Semantic Policy and Action Representations for Autonomous Robots (SPAR)
September 27, 2021 - Prague, Czech Republic
at IROS 2021
Tetsuya Ogata
Toward Embodied Intelligence with Predictive Learning – From Data to Experiences
In order to adapt to the complex real world, it is essential not only to acquire an optimal behavior policy by machine learning etc., but also to adjust the behavior itself in real time based on the policy of prediction error minimization from the viewpoint of experience which is interaction between the body and the environment. In this talk, I will introduce an overview of deep predictive learning (DPL) proposed by the authors to realize such "embodied intelligence". I will also introduce the examples of our work with several companies using DPL, the latest research results on tool use and flexible object handling, and an overview of our proposal AIREC (AI-driven Robot for Embrace and Care) in the "Moonshot", a large-scale R&D program in Japan.
Biography
Tetsuya Ogata received the B.S., M.S., and D.E. degrees in mechanical engineering from Waseda University, in 1993, 1995, and 2000, respectively. He was a Research Associate with Waseda University from 1999 to 2001. From 2001 to 2003, he was a Research Scientist with the RIKEN Brain Science Institute. From 2003 to 2012, he was an Associate Professor with the Graduate School of Informatics, Kyoto University. Since 2012, he has been a Professor with the Faculty of Science and Engineering, Waseda University. Since 2017, he is a Joint-Appointed Research Fellow with the Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology. Since 2020, he is a director of the Institute of AI and robots, Waseda University. His current research interests include human-robot interaction, dynamics of human-robot mutual adaptation, and inter-sensory translation in robot systems with neuro-dynamical models.