Human Robot Interaction
Fall 2025. 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. 26
- Course Overview
- Syllabus
- Aug. 28
- Single-Agent Sequential Decision-Making
- Sept. 2
- Value Iteration, Reinforcement Learning
- Sept. 4
- POMDPs, Probability, Bayesian Inference
- Sept. 9
- Intent Inference & Expression
- Sept. 11
- Intent Inference & Expression
- Paper Reading Expressing Thought, Functional Expressive Motion
- Sept. 16
- Reward and Policy Learning
- Sept. 18
- Experimental Design
Prediction for Action
- Sept. 23
- Guest Lecture Trajectory Forecasting
- Sept. 25
- Trajectory Forecasting
- Paper Reading Confidence-Aware Prediction, MultiPath
- Sept. 30
- Collaboration, Assistance, & Coordination
- Oct. 2
- Shared Autonomy
- Paper Reading Shared Autonomy via Hindsight Optimization, LILA
- Oct. 7
- HRI as a Game
- Oct. 9
- HRI as a Game
- Paper Reading Cooperative Inverse RL, Planning for AVs that Effect Humans
- Oct. 14
- No Class Fall Break 🍂
- Oct. 26
- No Class Fall Break 🍂
Learning, Alignment, and Safety
- Oct. 21
- Alignment
- Oct. 23
- Alignment
- Paper Reading Deep RL from Preferences, Learning from Physical HRI
- Oct. 28
- Representation Learning
- Oct. 30
- Nov. 4
- No Class Democracy Day
- Nov. 6
- HRI in the Era of Foundation Models
- Nov. 11
- Guest Lecture Active Learning
- Nov. 13
- Active Learning
- Paper Reading Active Learning from Demonstratons, Asking Easy Questions
- Nov. 18
- Safety & Uncertainty in HRI
- Safety with Agency
- Nov. 20
- Safety & Uncertainty in HRI
- Paper Reading Robots that Suggest Safe Alternatives, Robots that Ask for Help
Research Skills
- Nov. 25
- Technical Writing & Presentation
- Nov. 27
- No Class Thanksgiving
Project Presentations
- Dec. 2
- Project Presentations
- Slides Due 11:59 pm ET, Dec 1
- Dec. 4
- Project Presentations
- Project Report Due Dec 11
Instructor
Teaching Assistant
