Syllabus

Table of Contents

  1. Overview
  2. Logistics
  3. Prerequisites
  4. Attendance
  5. Academic Integrity
  6. Late Policy
  7. Accommodations for Students with Disabilities
  8. Communication
  9. Grading
  10. Paper Discussion Days
  11. Health & Wellness

Overview

Robot interaction with people is inevitable: human engineers iteratively tune robot policies, autonomous cars navigate through crowded cities, construction workers teleoperate drones for building inspections, and assistive robots help end-users with daily living tasks. In this class we will formalize such human-robot interaction problems algorithmically. We will build the mathematical foundations for modeling human-robot interaction across robots and tasks, enable robots to understand human intent and predict human behavior, and study how robot learning changes in the presence of human feedback. The approaches covered will draw upon a variety of disciplines and tools such as sequential decision-making, cognitive science, Bayesian inference, and modern machine learning. Throughout the class, there will also be several guest lectures from experts in the field. Students will practice essential research skills including reviewing papers, writing project proposals, and technical communication.

Logistics

  • Title: Human Robot Interaction, Fall 2025
  • Course Number: 16-867
  • Lecture: 11:00AM–12:20PM EST, Tues & Thurs
  • Location: NSH 3002
  • Office Hours: Please see “Schedule” tab

Prerequisites

There are no official prerequisites but expecting familiarity with robotics and related AI topics (e.g., sequential decision-making / planning, basic machine learning); willingness to conduct a significant final project outside of class time; willingness to read scientific papers and engage in in-class discussions; willingness to conduct in-class presentations about scientific literature. Experience with high-level programming languages like Python are also strongly encouraged.

Attendance

Class attendance and participation are key for both your and your peer’s success in this class. You are expected to attend class in person during the scheduled time, including the final presentations. I understand that occasionally you may have challenges attending (e.g., illness, religious observance, etc.). However, if you anticipate having a challenge regularly attending class, please contact me.

Academic Integrity

Honesty and transparency are important features of good scholarship. On the flip side, plagiarism and cheating are serious academic offenses with serious consequences. If you are discovered engaging in either behavior in this course, you will earn a failing grade on the assignment in question, and further disciplinary action may be taken. We encourage you to work together on projects and homework assignments and to make use of campus resources like Student Academic Success Center (SASC) to assist you in your pursuit of academic excellence. However, please note that in accord with the university’s policy you must acknowledge any collaboration or assistance that you receive on work that is to be graded, either from a person, reference, or a tool (including AI-generation tools like ChatGPT).

Late Policy

All homeworks and assignments are assigned due dates and should be submitted through the relevant Canvas portal. If you cannot submit an assignment on time, my default will be to reduce the grade by 10% for each 24 hour period, up to three days, that the assignment is late. This will be automatically applied; you do not have to request it. After three days, the assignment will receive a zero. If you experience an unforseeable emergency and would like me to consider waiving the late penalty, please email me as early as possible to discuss this request. The 10% per day deduction does not apply to unexcused late presentations, which will receive a zero immediately, because they will affect our ability to hold class. Re-scheduling presentations will be based on schedule availability and the professor’s discretion.

Accommodations for Students with Disabilities

If you would like to receive accommodation for a documented disability, please first contact Disability Resources (access@andrew.cmu.edu or 412-268-2013). Let us know as soon as possible so we can discuss reasonable accommodations. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at access@andrew.cmu.edu.

Communication

  • Website: We will use the class website for posting course content (e.g., lecture notes, paper readings, lecture recordings).
  • Canvas: We will use Canvas for uploading all assignments and grades.
  • Email: If you email your instructors, please include the substring “[HRI Course]” to begin a meaningful subject line and have tried to resolve the issue appropriately otherwise. Please use your CMU email account.

Grading

This course will have no exams. Instead, grading will be broken down by the following categories, with maximal emphasis on a project that is completed throughout the semester.

PercentageActivity
10%Attendance & Participation
20%HW (2x)
10%Paper Summaries
5%Project Proposal
20%Midterm Project (Report + Presentation)
35%Final Project (Report + Presentation)
  • Attendance & Participation: We want students to attend lectures and paper discussions in person consistently for the benefit of all taking the class. Students are permitted 2 unexcused absences, no questions asked, before being docked.
  • Homeworks: This course will have a few Python-based coding homeworks that allow students to apply techniques from the class.
  • Paper Summaries & Discussions: One goal we have for this course is for you to understand how to consume, explain, and critique research papers. There will be several paper discussion days during which you will be assigned research papers to read. Paper discussions occur in-class. Before a paper discussion day, you are expected to complete all assigned readings and submit paper summaries to Canvas by 10am ET of the day the reading will be discussed. The paper summaries must answer the following questions:
    1. What assumptions were made about (a) robot/algorithm, (b) human, (c) their interaction?
    2. What extensions would you propose? Scope & justify.
    3. What do you like about this work?
  • Project: Students will engage in a semester-long research project related to the themes of the course before presenting them at the end of the semester. The class project can be either an exploration of an original research idea or a thorough literature review (∼ 50 relevant papers, organized so that it identifies gaps in the state of the art). You must work in a group of min 2 and max 5 people. Explicit permission from Andrea should be granted if you want to work on an independent project. To help you make progress throughout the semester, we have three check-points:
    • Project Proposal: Early in the semester, students will submit a project proposal / pitch via Canvas.
    • Midterm Project Check-in: Midway through the semester, students will submit a mid-term report via Canvas and give an oral presentation in-class about the status of their project.
    • Final Project: The final project consists of an oral project presentation as well as a final project report of the length of a typical robotics or machine learning conference paper (6 pages).

Paper Discussion Days

During paper discussion days, we will dive into two papers. During the very first discussion day, we will randomly assign you into groups that you will keep throughout the semester. On each paper discussion day, there will be a set of discussion topics we have generated for each of the papers. In your group, you will discuss the assigned topics. In each group, one person will be randomly assigned to be the group representative who, after the in-class discussion period, will come up and present on the group’s conclusions. The whole class will engage the presenter on their conclusions and takeaways. (Note: this paper presentation structure is subject to change based on class size).

Health & Wellness

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:

CaPS: 412-268-2922

Re:solve Crisis Network: 888-796-8226