Human Robot Interaction
Fall 2026. 16-867. Tuesday / Thursday 11:00am-12:20pm.
Announcements
Course 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 graduate class, we will formalize such human-robot interaction (HRI) 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.
In summary, in this class you will learn how to:
- 👩🔬 Mathematically model HRI
- 🧠 Predict human behavior & infer intent
- 🤝 Robot learning from human feedback
- 🙌 and more!
Prerequisites
The course is open to graduate students and advanced undergraduates. There are no official prerequisites but we expect 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.
Schedule (Tentative)
Foundations
- Aug. 25
- Aug. 27
- Single-Agent Decision-Making [Notes]
- Ch. 17 Russel & Norvig, Ch. 1.1 Bertsekas
- Sept. 1
- Value Iteration, Reinforcement Learning [Notes]
- Test Your Knowledge (from CS188, Berkeley)
- Sept. 3
- POMDPs | Project Ideation Exercise [Notes]
- Sept. 8
- Probability, Bayesian Inference [Notes]
- Goal Inference as Inverse Planning
- Sept. 10
- Intent Inference & Expression [Notes]
- HW #1 DUE Expressing Thought, Functional Expressive Motion, Predictability & Legibility
- Sept. 15
- Reward and Policy Learning
- An Invitation to Imitation, MaxEntIRL, SAILOR
- Sept. 17
- Experimental Design & Statistical Analysis
- Project Proposal Due A Primer for Conducting Experiments in Human–Robot Interaction
Prediction for Action
- Sept. 22
- Collaboration, Assistance, & Coordination
- Sept. 24
- Guest Lecture Trajectory Forecasting (Ingrid Navarro, CMU)
- Sept. 29
- Trajectory Forecasting
- Paper Reading Confidence-Aware Prediction, ManiCast
- Oct. 1
- HRI as a Game
- Planning for AVs that Effect Humans, Contingency Games, iLQGames
- Oct. 6
- Shared Autonomy
- Paper Reading Policy Blending, LILA
- Oct. 8
- Mid-term Project Presentations
- Mid-Term Presentation Due
- Oct. 13
- No Class Fall Break 🍂
- Oct. 15
- No Class Fall Break 🍂
Learning, Alignment, and Safety
- Oct. 20
- Oct. 22
- Oct. 27
- Oct. 29
- Alignment
- Paper Reading Deep RL from Preferences, Learning from Physical HRI
- Nov. 3
- No Class Democracy Day
- Nov. 5
- Guest Lecture Active Learning (Erdem Bıyık, USC)
- Nov. 10
- Active Learning
- Paper Reading Active Learning from Critiques, Asking Easy Questions
- Nov. 12
- Guest Lecture (tentative) Latent Spaces Meet HRI (Guy Rosman, Toyota Research Institute)
- HW #2 Due: Nov 15 (Sunday) Dream2Assist
- Nov. 17
- Uncertainty in HRI
- Robots that Ask for Help, When to Act, Ask, or Learn
- Nov. 19
- Safety in HRI
- Last Day for Project Feedback: Nov 20 (Friday) Safety with Agency, Robots that Suggest Safe Alternatives
- Nov. 24
- Cancelled Class Thanksgiving
- Nov. 27
- No Class Thanksgiving
Project Presentations
- Dec. 1
- Project Presentations
- Slides Due 11:59 pm ET, Nov 30 Presenters: TBA
- Dec. 3
- Project Presentations
- Project Report Due Friday, Dec. 11 Presenters: TBA
Instructor
Teaching Assistant
