Robot Learning credit:

4 Hours

Recent developments in learning-based robotics with focus on computer vision, machine learning, robotics and control theory. Advanced techniques for learning policies for robots, including model-free reinforcement learning (RL) with function approximators, model learning, model-based RL with learned models, imitation learning, inverse RL, self-supervised learning, exploration, and hierarchical reinforcement learning. The course will conclude with case-studies on robotic navigation and manipulation from recent papers. Prerequisite: ECE 448 or ECE 549 or ECE 544 NA (Pattern Recognition).