Overview

Robots are moving from factory floors, battlefields, and space into human environments such as homes, offices, schools, and hospitals. How can we design robotic systems for human interaction?

In this course, students learn about the core engineering, computational, and experimental methods in human-robot interaction (HRI), focusing on robot  reasoning, AI, and machine learning aspects.  Students also learn how to design and analyze experiments to evaluate HRI systems.

Lectures cover key algorithms in Probabilistic Robotics, including Bayesian Networks, Markov Models, HMMs, Kalman and Particle Filters, MDP and POMDP, Supervised, Unsupervised and Reinforcement Learning.  Lectures on evaluating human-robot interaction systems include Experimental Design Methods and Metrics, Descriptive and Inferential Statistics  for experiment analysis such as Correlation, T-Test, ANOVA, General Linear Models, and Linear Regression.   The course pays special attention to the shared probabilistic foundations of both algorithmic and experimental aspects of HRI. Lectures emphasize the development of a strong intuition and understanding, with a significant portion of self-study left for details.

Throughout the class, we discuss seminal and recent papers in HRI making use of the learned methods and techniques, covering the following topics: machine perception for HRI, generating intentional action, reasoning about humans, 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 present papers in class and work in teams on an HRI research project development and evaluation.

Learning Outcomes

  • Find, read, and comprehend a technical HRI Research Paper
  • Familiarize yourselves with the main probabilistic algorithms driving computational HRI
  • Implement a human-robot interaction system in ROS
  • Plan and execute a human-subject study
  • Analyze the results of a study using inferential statistics
  • Present a research paper in a 20 minute conference-style presentation
  • Critically review a paper and comment on its advantages and shortcomings
  • Critical thinking!

Time and Location: MW 2:45-4:00pm, Online.

Prerequisites:  Python programming experience. This seminar-style project-based course is intended for PhD, M.S., and M.Eng. students from a variety of disciplines, including MAE, ECE, CS, and IS.

Required Readings:

  • Computational Human-Robot Interaction. Thomaz, Hoffman, & Cakmak, Foundations and Trends in Robotics. Vol 4: No. 2-3. Now Publishers, 2016.
  •  Artificial Intelligence: a Modern Approach. Russell & Norvig, Prentice Hall, 2009.
  • Discovering Statistics Using R. Field, Miles, & Field, Sage, 2012.

Additional Readings:

  • Probabilistic Robotics. Thrun, Burgard, & Fox, MIT Press, 2005.
  • Programming Robots with ROS: A Practical Introduction to the Robot Operating System. Quigley, Gerkey, & Smart, O’Reilley, 2015.
  • Reinforcement learning: An introduction (2nd ed.). Sutton & Barto. MIT Press, 2018. [Online]

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): Several 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 pairs or teams.  An online sign-up sheet will be sent out after the first class for students  and students need to sign up at the beginning of the second week. All substitutions must be cleared at least 10 days prior to the presentation.
  • Programming assignments (20% of the final grade): Four programming projects will be assigned during the time period prior to your project proposal.  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.
  • Class participation (10% of the final grade): At each class meeting, students who are not presenting are expected to read papers and participate in the in-class discussion of the paper.  Both attendance and lively participation will be noted.
  • Paragraph + discussion question (15% of the final grade):  To facilitate class participation, students will submit writing assignments. The week that you are presenting a paper you are excused from this writing assignments. There are no late assignments accepted. You need to submit one paragraph about one of the papers each week to a discussion forum, in addition to a discussion question about a paper presented in class.
  • Final project (30% of the final grade):  Students will develop a research project. Students have the choice between (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.  Several robot platforms 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 April 12 and will be reviewed by the instructor.  The project will conclude with an oral presentation to the class during the final week of classes, 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, at least 24 hours before class time or assignment due time.

List of topics:

  • Introduction: Human-Robot Interaction (HRI)
  • Key Concepts in Probability Theory
  • Bayesian Networks and Filtering
    • Bayesian Networks
    • Markov Models
    • Hidden Markov Models (HMMs)
    • Bayes Filtering
    • Kalman Filters
    • Particle Filters
  • Intentional Action
    • Intention Recognition
    • Intention Expression
    • Legible Robot Motion
  • Nonverbal Behavior in HRI
    • Social Navigation
    • Gestures and Body Language
    • Gaze and Eye Contact
  • Markov Decision Processes
    • Markov Decision Processes (MDPs)
    • Partially Observable Markov Decision Processes (POMDPs)
  •  Collaboration
    • Theoretical Foundations of Collaboration
    • Human-Robot Collaboration
    • Handovers
    • Multimodal Dialog
  • Machine Learning
    • Clustering
    • Supervised Learning
    • Reinforcement Learning
  • Learning in HRI
    • Learning from Demonstration
    • Socially Guided Robot Learning
  • HRI Experiments
    • Experimental Design
    • Metrics and Measurement
    • Inferential Statistics
    • T-test
    • General Linear Model (GLM)
    • Analysis of Variance (ANOVA)

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).