This event is organized by the University of Toronto Robotics Institute

Robotics Institute Seminar Series with Peter Stone

Date: Friday, September 13, 2024

Time: 3 p.m. – 4 p.m.

Location: Online (Zoom)

Talk title: Human-in-the-Loop Learning for Robot Navigation and Task Learning from Implicit Human Feedback

Abstract:

While end-to-end, fully autonomous learning is interesting to explore, for real-world applications, including robotics, the paradigm of human-in-the-Loop learning has emerged as a practical way of guiding and speeding up the learning process.  This talk will introduce some recent human-in-the-loop learning algorithms that enable robust navigation in challenging settings, such as in densely cluttered environments and over varying terrains.  While most of these algorithms take explicit input from human trainers, the talk will close with a new paradigm for reinforcement learning from implicit human feedback, specifically observed facial expressions.

Bio:

I am the founder and director of the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory in the Department of Computer Science at The University of Texas at Austin, as well as associate department chair and Director of Texas Robotics.

I was a co-founder of Cogitai, Inc. and am now Chief Scientist of Sony AI.

My main research interest in AI is understanding how we can best create complete intelligent agents. I consider adaptation, interaction, and embodiment to be essential capabilities of such agents. Thus, my research focuses mainly on machine learning, multiagent systems, and robotics. To me, the most exciting research topics are those inspired by challenging real-world problems. I believe that complete successful research includes both precise, novel algorithms and fully implemented and rigorously evaluated applications. My application domains have included robot soccer, autonomous bidding agents, autonomous vehicles, and human-interactive agents.

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