Recent developments in machine learning and perception for robotics, studying advanced perception and decision-making algorithms to provide theoretical and experimental frameworks for robot learning. Topics include 3D vision, sensorimotor paradigms for perception and action, robot reinforcement learning, imitation learning, inverse reinforcement learning, exploration, model-based approaches, POMDPs, and human-machine and social interaction.

Level

grad

Department

ENG

Credits

4

Prerequisites

MA 225, EK 103, EK 381, EK 128, EC 414