Institutionen för naturvetenskap och teknik

AASS Seminar - Co-designed head to toe: Towards end-to-end participatory design

15 september 2022 13:00 Zoom

For more information about the AASS Seminar Series, please contact:
Alessandro Saffiotti

The research centre AASS arranges a seminar with Séverin Lemaignan, PAL Robotics, Spain.

Join online
(Dial-in +46 8 5016 3827, Meeting ID: 650 9662 3293)

Abstract

Participatory methodologies are now well established in social robotics to generate blueprints of what robots should do to assist humans. The actual implementation of these blueprints, however, remains a technical challenge for us, roboticists, and the end-users are not usually involved at that stage. In two recent studies, we have however shown that, under the right conditions, robots can directly learn their behaviours from domain experts, replacing the traditional heuristic-based or plan-based robot controllers by autonomously learnt social policies. We have derived from these studies a novel 'end-to-end' participatory methodology called LEADOR, that I will introduce during the seminar. Time permitting, I will also briefly discuss recent progress on human perception and modeling in a ROS environment with the emerging ROS4HRI standard.

Speaker's bio

Since 2021, Dr. Séverin Lemaignan is Senior Scientist at Barcelona-based PAL Robotics. He leads the Social Intelligence team, in charge of designing and developing the socio-cognitive capabilities of robots like PAL TIAGo and PAL ARI. He was previously Associate Professor in Social Robotics and AI at the Bristol Robotics Laboratory, University of the West of England, Bristol. He obtained in 2012 a joint PhD in Cognitive Robotics from the CNRS/LAAS (France) and the Technical University of Munich (Germany). He then joined the EPFL (Switzerland) and Plymouth University (UK) as post-doc, then lecturer in Robotics until 2018, when he joined the Bristol Robotics Lab. His research interest primarily concerns socio-cognitive human-robot interaction, child-robot interaction and human-in-the-loop machine learning for social robots.