School of Engineering
Human-Robot Interaction Program
Human-Robot Interaction Program

Course Requirements

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: CS 133 Human-Robot Interaction 
     
  • HRI 202 Robot design and control 
    Synopsis: fundamentals of robot design and robot control 
    Current offering: ME 134: Advanced Robotics 
     
  • HRI 203 Robot programming 
    Synopsis: an in-depth coverage of modern AI/probabilistic robotics algorithms 
    Current offering: CS 141 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
     
  • 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: CS 239-01: Ethics for AI, Robotics, and Human-Robot Interaction 
    Note: Graduate students must take the graduate version of the course, CS 239-01, the undergraduate version CS 139-01 will no longer be counted as the HRI 205 HRI Ethics core course requirement.

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.

Electives

All HRI students are required to take at least five HRI electives from a pool of approved courses. Below are the lists of electives for HRI students. PhD students may take any of these electives. In choosing electives, MS students should refer to their department-specific MS guidelines, which refer to the lists below.

Students are allowed to count at most one independent study research course in CS, ECE, and ME as an HRI elective as long as it is on a topic relevant to HRI for which there are no approved electives. Students interested in taking such a course should consult with their HRI advisor before signing up to ensure that it can be counted.

Computer Science

  • CS 131: Artificial Intelligence (3 SHUs)
  • CS 132: Computer Vision (3 SHUs)
  • CS 134: Computational Models in Cognitive Science (3 SHUs)
  • CS 135: Introduction to Machine Learning and Data Mining (3 SHUs)
  • CS 136: Statistical Pattern Recognition (3 SHUs)
  • CS 137: Deep Neural Networks (3 SHUs)
  • CS 138: Reinforcement Learning (3 SHUs)
  • CS 150-DL: Deep Learning for Computer Vision (3 SHUs)
  • CS 150-DR: Developmental Robotics (3 SHUs)
  • CS 150: HCI for Disability
  • CS 150: Special topics: Assistive Algorithms (3 SHUs) 
  • CS 150: Special topics: Epistemic Planning (3 SHUs)
  • CS 150: Special topics: Logic for AI (or CS + AI) (3 SHUs)
  • CS 150: Special topics: Natural Language Processing (3 SHUs)
  • CS 150: Special topics: Trust in Human-Robot Interaction (3 SHUs)
  • CS 152: Cognitive Architectures in the Age of Foundation Models (3 SHUs)
  • CS 160: Algorithms (4 SHUs)
  • CS 171: Human-Computer Interaction (3 SHUs)
  • CS 250-AFI: Affective Interfaces (3 SHUs)
  • CS 250-HCI: Human-Computer Interaction Seminar (3 SHUs)
  • CS 250-MLS: Machine Learning Seminar (3 SHUs)
  • CS 250-PBI: Physiological and Brain Interfaces (3 SHUs)

Electrical and Computer Engineering

  • EE-0105: Feedback-Control Systems (3 SHUs)
  • EE-0106: Advanced Feedback-Control Systems (3 SHUs)
  • EE-0109: Convex Optimization (3 SHUs)
  • EE-0125: Digital Signal Processing (3 SHUs)
  • EE-0133: Digital Image Processing (3 SHUs)
  • EE-0107: Communication Systems (4 SHUs)
  • EE-0127: Information Theory (3 SHUs)
  • EE-0130: Networked Estimation and Control (3 SHUs)
  • EE-0294: Special Topics: System Identification (3 SHUs)

Engineering Psychology

  • ENP105: Assistive Technology (3 SHUs)
  • ENP110: Human Factors in Medical Technology (3 SHUs)
  • ENP114: Ergonomics in Design (3 SHUs)
  • ENP149: Design for Ecological Interface (3 SHUs)
  • ENP161: Human Factor Product Design (3 SHUs)
  • ENP162: Human-Machine System Design (3 SHUs)
  • ENP163: Analytical Methods in Human Factors Engineering (3 SHUs)
  • ENP166: Computer Interface Design (3 SHUs)

Mechanical Engineering

  • ME121: Advanced Dynamics (3 SHUs) / listed as ME181 prior to 2020
  • ME122: Advanced Vibrations (3 SHUs)
  • ME123: Biomechanics (3 SHUs)
  • ME130: Digital Control Of Dynamic Systems (3 SHUs) / listed as ME180 prior to 2020
  • ME133: GPS & Satellite Navigation (3 SHUs) / listed as ME186 prior to 2020
  • ME140: Inventive Design (3 SHUs) / listed as ME102 prior to 2020
  • ME193: Assistive Design (3 SHUs)
  • ME193: Educational Robotics (3 SHUs)
  • ME193: GPS & Inertial Navigation Systems (3 SHUs)

Psychology

  • PSY-212: Human Communication

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.