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.

Speaker bio

Peter Stone is 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.

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

His main research interest in AI is understanding how we can best create complete intelligent agents. He considers adaptation, interaction, and embodiment to be essential capabilities of such agents. Thus, Stone’s research focuses mainly on machine learning, multiagent systems, and robotics. His application domains have included robot soccer, autonomous bidding agents, autonomous vehicles, and human-interactive agents.