CS 6754

Human-Robot Interaction: Algorithms and Experiments

Course Description

Course information provided by the Courses of Study 2024-2025.

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.

When Offered Fall.

Permission Note Enrollment limited to: graduate students or seniors with permission of instructor.
Prerequisites/Corequisites Prerequisite: Python programming experience.

• Students will be able to find, read, and comprehend a technical HRI Research Paper.
• Students will be familiar with the main probabilistic algorithms driving computational HRI.
• Students will be able to implement a HRI system in ROS.
• Students will be able to know how to plan and execute a human-subject study and analyze the results of a study using inferential statistics.
• Students will be proficient at presenting a research paper in a 20-minute conference-style presentation.
• Students will be able to know how to critically review a paper and comment on its advantages and shortcomings.

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