ESR 9

Manuel Bied

Learning from and about humans for robot task learning

Bio

Manuel Bied is a PhD student at Institut des Systèmes Intelligents et de Robotique (ISIR) at Sorbonne Université supervised by Prof. Mohamed Chetouani. In his research he’s interested in integrating pedagogical reasoning with robot learning strategies as Reinforcement Learning and Learning-from-Demonstration. He is part of the MSCA-Innovative Training Network ANIMATAS. Prior to joining ISIR he received a B.Sc. and M.Sc. degree in Electrical Engineering from TU Darmstadt (Germany). His Master’s thesis about Learning-from-Demonstration was conducted in cooperation with Honda Research Institute Europe and supervised by Prof. Jan Peters.

Main host institution

Sorbonne Université (SU)

Supervisor

Mohamed Chetouani (UPMC), in association with Amit Pandey (SBR)

Second institution

SBRE

Objectives

There is growing of interest for Robot Learning from Demonstration with the aim of developing machines that are able to take advantage of human interactions. Interpersonal skills and the nature of the task impact the performances. Our main objective is to investigate the role of interpersonal interaction in task learning/sharing situations and in particular the impacts on interactive learning schemes. We will focus on social learning mechanisms that could allow a robot to learn a task from interactions with humans (in particular teachers) but also inform about humans (e.g. identity, personality traits, engagement and expertise). We will explore socially guided machine learning techniques (neural networks, reinforcement learning). Interpersonal interactions (including task sharing) will be explicitly modeled at the feature (e.g., synchrony between actions) or decision (e.g., rewards) levels. Multi-task learning framework will be employed for learning social traits and tasks. The final aim is to propose strategies for bootstrapping robot task learning. The models will be evaluated in experimental settings with human teachers demonstrating various sets of tasks (object recognition, sequence learning) to robots.

Expected results

Completed PhD dissertation; computational model allowing robot to learn from adult teachers demonstration and guidance; peer-reviewed publications