Interactive Robot Learning

Summary: In this talk, we focus on main methods and models enabling humans to teach embodied social agents such as social robots, using natural interaction. Humans guide the learning process of such agents by providing various teaching signals, which could take the form of feedback, demonstrations and instructions. This overview describes how human teaching strategies are incorporated within machine learning models. We detail the approaches by providing definitions, technical descriptions, examples and discussions on limitations. We also address natural human biases during teaching. We then present applications such as interactive task learning, robot behavior learning and socially assistive robotics. Finally, we discuss research opportunities and challenges of interactive robot learning.

Bio: Prof. Mohamed Chetouani is currently a Full Professor in signal processing and machine learning for human-machine interaction. He is affiliated to the PIRoS (Perception, Interaction et Robotique Sociales) research team at the Institute for Intelligent Systems and Robotics (CNRS UMR 7222), Sorbonne University (formerly Pierre and Marie Curie University). His activities cover social signal processing, social robotics and interactive machine learning with applications in psychiatry, psychology, social neuroscience and education. He was the coordinator of the ANIMATAS H2020 Marie Sklodowska Curie European Training Network (2018-2022). Since 2019, he is the President of the Sorbonne University Ethics Committee. He was involved in several educational activities including organization of summer schools. He is member of the EU Network of Human-Centered AI. He is General Chair of ACM ICMI 2023. He is in charge of the inclusion of Students with Disabilities for the Faculty of Science and Engineering of Sorbonne University.