Par : Greg Dudek
In this talk I will present work we have done on developing intelligent systems that learn to operate in complex environments in response to human instruction and perceptual feedback. In particular, I will discuss some of the ways we have exploited machine learning such as reinforcement learning to refine the performance of the both to learn high-performance navigation strategies, gaits, and a notion of curiosity-driven exploration. I will discuss how we allow the vehicle to track human and other targets in the ocean using visual target recognition, how we have addressed walking and swimming, and how the vehicle can model human level of trust (satisfaction) in attempt to personalize it’s behavior. If time permits, I will also discuss an approach to efficient learning of human behavioural preferences and proxemics to develop robotics that are suited to operating for and with people.
Greg Dudek is the James McGill Chair in Computer Science at McGill University, Director of the NSERC Canadian Robotics Network (NCFRN) and Vice President for Research and head of the Artificial intelligence at Samsung AI Center in Montreal. His research is on sensor-based robotics using computer vision and machine learning, as well as decision-making under uncertainty.
Jeudi 13 janvier, 10 h 30.
Lien zoom : https://uqam.zoom.us/j/85407268175. Connectez-vous 10 à 15 minutes à l’avance, et donnez votre nom complet dès votre entrée pour nous faciliter la tâche de vous admettre au séminaire.