Robots are moving from factory floors, battlefields, and space missions into human environments such as homes, offices, schools, hospitals, construction sites, and workshops. How can we design robotic systems made for human interaction?
In this course, students learn about the core engineering, computational, and experimental challenges and techniques in human-robot interaction (HRI). These span the robot’s perception, reasoning, intelligent behavior, and machine learning aspects.
We discuss seminal and recent papers on: generating intentional action, social navigation, teamwork and collaboration, collaborative manipulation, machine learning with humans in the loop, embodied verbal and nonverbal dialog, and application areas for HRI. Students also learn how to design experiments to evaluate human-robot interaction systems.
Students present papers in class and work in teams on a research project concerning human-robot interaction development and evaluation.
As a first-time experiment, in Fall 2016 this course will be run in coordination between UT Austin (ECE 382V, Prof. Andrea Thomaz) and Cornell (MAE 6950, Prof. Guy Hoffman). This website represents the joint information and resources shared between the two courses. Additional details specific to each course will be made available by each instructor separately.
Cornell Time and Location: TR 10:10–11:25am, Hollister 362. Office Hours: R 2:00-3:30p
UT Austin Time and Location: TR 3:30–5:00pm, PAR 101
Slack Team: hri16
Prerequisites: Python programming experience. Co-requisite: Introductory AI and/or Machine Learning course. This seminar style course is intended for PhD, M.S., and M.Eng. students from a variety of disciplines, including MAE/ME/ASE, ECE, CS, and IS.
- Foundations and Trends: Social Robotics. Thomaz, Hoffman, & Cakmak, Now Publishers (Forthcoming), 2016.
- Artificial Intelligence: a Modern Approach. Russell & Norvig, Prentice Hall (2009).
- Programming Robots with ROS: A Practical Introduction to the Robot Operating System. Quigley, Gerkey, & Smart, O’Reilley, 2015.
- Discovering Statistics Using R. Field, Miles, & Field, Sage, 2012.
Papers and other material are available on the Reading List page.
Grading: The grade will be determined based on the following:
- Paper presentations (25% of the final grade): Ten lectures will be devoted to paper presentations by students. These papers, representing seminal and current work in the field, will complement the material covered in class. Due to size of the class, papers will be presented in teams. A doodle poll will be sent out after the first class for students to sign up, and students need to sign up before the second meeting. Grading of this assignment will follow the rubrics posted on the course site. Papers will be assigned according to the reading list; however, students may choose to present a different paper related to the same topic. All substitutions must be cleared at least 10 days prior to the presentation.
- Programming assignments (10 % of the final grade): Three programming projects will be assigned during the time period prior to the midterm exam. These will be introductory exercises to familiarize students with programming a simulated robot in Python with the ROS programming environment. These are meant as preparation for conducting a more complex project/experiment for the final project in the class.
Midterm Exam (25% of the final grade): This will be an in-class exam covering both the foundation lectures and the papers presented by students. You are permitted to bring any number of written pages of notes to use during the exam.
- Class participation (10% of the final grade): At each class meeting, students who are not presenting are expected to read the paper and participate in the in-class discussion of the paper. Both attendance and participation will be noted.
- Final project (30% of the final grade): Development of a research project by students. Students have the choice of (a) extending an existing method or algorithm from one of the papers presented in class and implementing it; (b) directly implementing a method or algorithm from one of the papers and running a study evaluating it; or (c) proposing a novel algorithm to implement and experiment with. Students will work in pairs. A robot platform will be introduced in class and made available for your use in these projects. You may use an alternate robot platform with permission of the instructor. A project proposal is due on Nov 1 and will be reviewed by the instructor. The project will conclude with an oral presentation to the class during the final exam period, as well as a written report.
Late policy: For fairness to all students, neither late work, nor late arrival to classes will be accepted. In case of exceptional circumstances, contact the professor or TA immediately in due time.
Tentative list of topics:
- Introduction: Human-Robot Interaction (HRI)
- Intentional Action
- Bayes Nets, Markov Models
- Intention Parsing
- Intention Expression
- Legible Robot Motion
- Social Navigation
- Navigation Planning
- Kalman and Particle Filters
- Social Navigation
- Nonverbal Behavior in HRI
- Gestures and Body Language
- Gaze and Eye Contact
- MDP, POMDP
- Theoretical Foundations of Collaboration
- Human-Robot Collaboration
- Learning in HRI
- Reinforcement Learning and Supervised Learning
- Learning from Demonstration
- Socially Guided Robot Learning
- HRI Experiments
- Experimental Design
- Metrics and Measurement
- Inferential Statistics
- Statistical Tests: T-test, GLM, ANOVA, Mediation and Moderation
- Applications for HRI
- Socially Assistive Robots
- HRI Ethnographies
- HRI in Commercial Spaces
Expectations: You can expect the instructor to start and end class on time, as well as grade your performance and send you feedback on your presentations in a timely manner. In turn, we can expect you to come to class on time, be attentive and engaged in class, and refrain from using laptops, cell phones and other electronic devices during class. I also expect you to spend an adequate amount of time on the readings each week (estimated: 2–3 hours) and on your final assignment (estimated: 40–60 hours).