Robot Learning

Graduate / Professional

This course explores methods for grounding machine learning algorithms in embodied, interactive robots. We cover topics including learning from demonstration, active learning, inverse reinforcement learning, representations for modeling high-level and low-level task information, and human factors for designing learning interactions. Students are asked to read and present research papers on these topics from top publication venues in AI, machine learning, robotics, and human-robot interaction. Students also complete lab assignments in which they implement and evaluate state-of-the-art methods for interactive robot learning on a physical robot arm. Prerequisite: CPSC 370/470/570 or an equivalent AI course (requires approval by instructor). Recommended: an introductory machine-learning course such as S&DS 265 or CPSC 481. TTh 1:05pm-2:20pm