Computational Human-Robot Interaction

Instructor: Stefanos Nikolaidis (snikolai at alumni dot cmu dot edu)

Lectures: Tue / Thu 10:00 - 11:50am LVL 13

Office Hours: Tue 13:30-15:30pm SAL 242 (by appointment)

Course Description: In this advanced graduate-level class, you will learn about the theory and algorithms that enable robots to account for people in their decision making in a principled way. The course will contrast decision-theoretic and learning-based paradigms that allow robots to reason in the presence of uncertainty with studies in human-robot interaction. It will then focus on what makes some of these algorithms particularly effective and scalable in real-world human-robot interaction scenarios. By the end of this class, you will be able to describe and compare algorithms for deployed robotic systems interacting with people, design user studies to evaluate these algorithms and communicate your ideas to a peer audience. Evaluation is mainly based on student presentations, a final project and short quizzes based on the assigned reading material.


Learning Objectives: In this course, you will gain knowledge about planning and learning algorithms in human-robot interaction and skills in interpreting and presenting research. By the end of this course you should be able to:

Prerequisites: There are no formal prerequisites, but knowledge of probability theory and linear algebra is encouraged.


Component Percentage
Paper Presentations 30%
Final Project 40%
Weekly Quizzes 15%
Participation 10%
Scribing 5%

Assessment of Assignments

Important Dates:

October 23rd: Project Proposal Submission. You are encouraged to meet with me before the submission to discuss project ideas.

Project Proposal:


Day Date Topic Reading Notes
Tue Aug 21st What is Computational HRI?
  • Computational Human-Robot Interaction, Thomaz, Hoffman and Cakmak. (Optional)
  • Human modeling for human–robot collaboration, Hiatt et al. (Optional)
  • The Grand Challenges in Socially Assistive Robotics, Tapus et al. (Optional)
  • Social robots that interact with people, Breazal et al. (Optional)
Scribing: template.tex   questionnaire slides notes
Thu Aug 23rd Probability and Bayesian inference
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapters 13, 14 and 15.
  • Real-Time American Sign Language Recognition from Video Using Hidden Markov Models, Starner and Pentland.
Tue Aug 28th Bayesian inference (cont'd) and decision making under uncertainty
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 16.
Thu Aug 30th Markov decision processes and applications in HRI
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 17.1- 17.3.
Tue Sept 4th Action selection for collaboration (student presentations)
  • Cost-Based Anticipatory Action Selection for Human-Robot Fluency, Hoffman and Breazal. (Sayan)
  • Anticipating human actions for collaboration in the presence of task and sensor uncertainty, Hawkins et al. (Xin)
  • Joint action: bodies and minds moving together, Sebanz et al.(Shihan)
Thu Sept 6th Experimental Design Sample consent form notes
Tue Sept 11th Training of human teams and shared mental models (student presentations)
  • The Impact of Cross-Training on Team Effectiveness, Marks et al. (Setareh)
  • The Influence of Shared Mental Models on Team Process and Performance, Mathieu et al. (Jessica)
  • Planning, Shared Mental Models and Coordinated Performance: An Empirical Link is Established, Stout et al. (Shixian)
Thu Sept 13th Action coordination in human-robot teams (student presentations)
  • Human-Robot Cross-Training: Computational Formulation, Modeling and Evaluation of a Human Team Training Strategy, Nikolaidis and Shah (Lauren)
  • Improved Human-Robot Team Performance Using Chaski, A Human-Inspired Plan Execution System, Shah et al. (Audrow)
  • Adaptive Coordination Strategies for Human-Robot Handovers, Huang et al. (Kiran)
Tue Sept 18th Intent inference (student presentations)
  • Goal Inference as Inverse Planning, Baker et al. (Yash)
  • 'Obsessed with goals': Functions and mechanisms of teleological interpretation of actions in humans, Csibra and Gergely. (Chris)
  • Planning-based Prediction for Pedestrians, Ziebart et al. (Nitu)
Thu Sept 20th Expressiveness in robot motion (student presentations)
  • Expressing thought: improving robot readability with animation principles, Takayama et al. (Naghmeh)
  • The Illusion of Robotic Life, Ribeiro and Paiva. (Emily)
  • Perception of Affect Elicited by Robot Motion, Saerbeck and Bartneck. (Leili)
Tue Sept 25th Generation of expressive motion (student presentations)
  • Generating Legible Robot Motion, Dragan and Srinivasa. (David)
  • Enhancing Interaction Through Exaggerated Motion Synthesis, Gielniak and Thomaz. (Arshdeep)
  • Communication of Intent in Assistive Free Flyers, Szafir1 et al. (Isabel)
Thu Sept 27th Planning with partial observability notes
Tue Oct 2nd Planning with partially observable human states (student presentations)
  • Intention-aware motion planning, Bandyopadhyay et al. (Tricia)
  • Belief Space Planning for Sidekicks in Cooperative Games, Macindowe et al. (Shixian)
  • Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process, Hoey et al. (Xin)
Thu Oct 4th Planning with human state dynamics (student presentations)
  • Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model, Nikolaidis et al. (Shihan)
  • Planning for Autonomous Cars that Leverage Effects on Human Actions, Sadigh et al. (Setareh)
  • Game-theoretic modeling of human adaptation in human-robot collaboration, Nikolaidis et al. (Jessica)
Tue Oct 9th Planning in shared autonomy domains (student presentations)
  • Shared Autonomy via Hindsight Optimization, Javdani et al. (Tom)
  • Autonomy Infused Teleoperation with Application to BCI Manipulation, Muelling et al. (Sayan)
  • Comparing shared control approaches for alternative interfaces: A wheelchair simulator experiment, Ezeh1 et al. (Lauren)
Thu Oct 11th Learning techniques for HRI
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 20.
  • The Expectation Maximization Algorithm, A short tutorial, Borman. (optional)
  • A survey of robot learning from demonstration, Argall et al.(optional)
Tue Oct 16th Learning from demonstration in HRI (student presentations)
  • Trajectories and Keyframes for Kinesthetic Teaching: A Human-Robot Interaction Perspective, Akgun et al. (Yash)
  • Confidence-based policy learning from demonstration using gaussian mixture models, Chernova and Veloso. (Kiran)
  • Learning to Tutor from Expert Demonstrators via Apprenticeship Scheduling, Gombolay et al. (Audrow)
Thu Oct 18th Active learning in HRI (student presentations)
  • Designing robot learners that ask good questions, Cakmak and Thomaz (Chris).
  • Active Preference-Based Learning of Reward Functions, Sadigh et al (Nitu).
  • Discovering task constraints through observation and active learning, Hayes and Scassellati (Naghmeh).
Tue Oct 23rd Reinforcement learning Project proposal submission
Thu Oct 25th Reinforcement learning with human feedback (student presentations)
  • Reinforcement Learning with Human Teachers, Thomaz and Breazeal. (Leili)
  • Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning, Knox and Stone. (Emily)
  • Interactive Learning from Policy-Dependent Human Feedback, MacGlashan et al. (David)
Tue Oct 30th Integrating learning and planning in HRI (student presentations)
  • Efficient model learning for dialog management, Doshi and Roy. (Arshdeep)
  • Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks, Nikolaidis et al. (Isabel)
  • Planning with Trust for Human-Robot Collaboration, Chen et al. (Tricia)
Thu Nov 1st Optimal teaching (student presentations)
  • Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications, sections 1-6, Brown and Niekum (Tom)
  • Algorithmic and human teaching of sequential decision tasks, Cakmak and Lopez. (Xin)
  • How do humans teach: On curriculum learning and teaching dimension, sections 1-3, Khan et al. (Kiran)
Tue Nov 6th Pedagogical reasoning (student presentations)
  • Cooperative inverse reinforcement learning, Hadfield et al. (David)
  • Pragmatic-Pedagogic Value Alignment, Fisac et al. (Arshdeep)
  • Showing versus doing: Teaching by demonstration, Ho et al. (Shihan)
Thu Nov 8th Communication and signaling (student presentations)
  • Effects of Robot Sound on Auditory Localization in Human-Robot Collaboration, Cha et al. (Sayan)
  • ConTaCT: Deciding to Communicate during Time-Critical Collaborative Tasks in Unknown, Deterministic Domains, Unhelkar and Shah (Yash)
  • Implicit Communication in a Joint Action, Knepper et al. (Emily)
Tue Nov 13th Socially assistive robotics (guest lecture: Maja Mataric)
Thu Nov 15th Natural language dialog systems (guest lecture: David Traum)
Tue Nov 20th Projects discussion
Thu Nov 22th no class
Tue Nov 27th Project presentations
Thu Nov 29th Project presentations
Fri Dec 7th Final report due

Expectations: You can expect me to come to class on time, clearly communicate expectations for the presentations structure, format and clarity, give you feedback on a timely manner, adjust lecture material based on performance on presentations and quizzes and be available to meet regularly to discuss the progress of your project. I can expect you to come to class on time, be attentive and engaged in class, take notes and ask questions when something is not clear, spend an adequate amount of time on the readings each week (at least 3 hours), spend 60-80 hours on your final project.

Additional Policies: Please see the syllabus for the statement on academic conduct and student support systems. Unless you are assigned to compile lecture notes, please refrain from using laptops or other electronic devices during class.

Related Courses: You are encouraged to expand your readings from related courses, for example: