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Course Requirements

A minimum of ten courses is required: five core courses in HRI and five electives from a list of courses approved by the HRI Steering Committee. Courses can be counted both towards the HRI Ph.D. and the home department. In addition, students must provide evidence of competence in four research methods.

Core Courses

All HRI students must take all five core courses listed. These are non-transferrable and represent five foundational subfields in HRI. These course will be offered regularly.
Students must take them when they are offered.

Note that the "HRI" course designation below does not currently exist, but is planned to be be introduced in the near future:
  • HRI 201 Introduction to Human-robot Interaction
    Synopsis: an overview introduction to human-robot interaction
    Current offering: COMP150-02 Human-Robot Interaction
  • HRI 202 Robot design and control
    Synopsis: fundamentals of robot design and robot control
    Current offering: ME184:Advanced Robotics
  • HRI 203 Robot programming
    Synopsis: an in-depth coverage of modern AI/probabilistic robotics algorithms
    Current offering: COMP 150-PR Probabilistic Robotics
  • HRI 204 Modeling for engineering systems
    Synopsis: Basic formal methods for analyzing and model autonomous systems
    Current offering: EE-0104: Probabilistic Systems Analysis, ME282 Optimal Control and State Estimation
  • HRI 205 HRI Ethics
    Synopsis: an in-depth coverage of all ethical aspects of human-robot interaction,
    from robot design to societal implications
    Current offering: COMP 150: Ethics for AI, Robotics, and Human-Robot Interaction

There will likely be additional substitutes for the above courses, especially since new courses might be offered in different departments as part of a seminar course series.

The decisions about which course to count towards the core course in HRI will be made by the HRI Steering Committee.


All HRI students are required to take at least five HRI electives from a pool of approved courses (at least three of the five electives must be from a department outside of the home department of the student):

Computer Science

  • COMP 131: Artificial Intelligence
  • COMP 135: Introduction to Machine Learning
  • COMP 136: Statistical Pattern Recognition
  • COMP 150-AAA: Artificial Agents and Autonomy
  • COMP 150-CMCS: Computational Models in Cognitive Science
  • COMP 150-CVI: Computer Vision
  • COMP 150-DL: Deep Learning for Computer Vision
  • COMP 150-DR: Developmental Robotics
  • COMP 150-NLD: Situated Natural Language Dialogues With Robots
  • COMP 150-NLP: Natural Language Processing
  • COMP 150-RML: Research in Applied Machine Learning
  • COMP 150-UIM: User Interfaces for Mobile Platforms
  • COMP 160: Algorithms
  • COMP 171: Human-Computer Interaction
  • COMP 250-AFI: Affective Interfaces
  • COMP 250-HCI: Human-Computer Interaction Seminar
  • COMP 250-MLS: Machine Learning Seminar
  • COMP 250-PBI: Physiological and Brain Interfaces

Electrical and Computer Engineering

  • EE-0105: Feedback-Control Systems
  • EE-0106: Advanced Feedback-Control Systems
  • EE-0109: Convex Optimization
  • EE-0125: Digital Signal Processing
  • EE-0133: Digital Image Processing
  • EE-0107: Communication Systems
  • EE-0127: Information Theory
  • EE-0130: Networked Estimation and Control
  • EE-0294: Special Topics: System Identification

Engineering Psychology

  • ENP105: Assistive Technology
  • ENP110: Human Factors in Medical Technology
  • ENP149: Design for Ecological Interface
  • ENP161: Human Factor Product Design
  • ENP162: Human-Machine System Design
  • ENP163: Analytical Methods in Human Factors Engineering
  • ENP166: Computer Interface Design

Mechanical Engineering

  • ME102: Inventive Design
  • ME137: Advanced Vibrations
  • ME149: GPS
  • ME149: Nonlinear System Dynamics
  • ME149: Optimal Control for Robotics
  • ME180: Digital Control Of Dynamic Systems
  • ME181: Advanced Dynamics
  • ME282: Optimal Control and State Estimation

It is expected that over time additional courses (from the above and other departments) will be approved by the HRI Steering committee and added to the list. For cross-listed electives, students can choose how to count them (i.e., for which department/program). For example, we expect to add courses from Psychology (e.g., "Social Cognition" or "Advanced Engineering Psychology"), OT ("Assistive Technology" or "Occupational Therapy Practice with Older Adults"), the Hitachi Center in the Fletcher School (e.g., "Technology and International Security" or "Technology Strategy and Innovation in Global Markets: Managing Innovation for Securing Global Competitive Advantage"), and other relevant programs. The electives will be guided by student interest to allow for the greatest possible flexibility in selecting and counting electives.