Human-Robot Interaction: Algorithms and Experiments

Catalog description

As robots move from factory floors and battlefields into homes, offices, schools, and hospitals, how can we build robotic systems made for human interaction? Course will cover core engineering, computational, and experimental techniques in human-robot interaction (HRI). Lectures will cover key algorithms in Probabilistic Robotics, including Bayesian Networks, Markov Models, HMMs, Kalman and Particle Filters, MDP and POMPD, Supervised Learning, and Reinforcement Learning. Seminal and recent papers in HRI will be discussed, including topics such as: generating intentional action, reasoning about humans, social navigation, teamwork and collaboration, machine learning with humans in the loop, and human-robot dialog. Students will learn methods for designing and analyzing HRI experiments. Presentation of papers in class, and an HRI-related research project in teams will be required. Intended for M.Eng to PhD students from multiple disciplines including MAE, CS, ECE and IS.