Computational Human-Robot Interaction

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

Lectures: Mon / Wed 08:00 - 09:50 (CPA 260)

TA: Ya-Chuan (Sophie) Hsu

Office Hours: Wed 10:00-11:00 (after class)

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 10%
Participation 10%
Homework 10%

Assessment of Assignments

Important Dates:

March 1st: Project Proposal Submission.

Project Proposal:


Day Date Topic Reading Notes
Mon Jan 10th 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)
Wed Jan 12th 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.
notes_A notes_B Slides
Mon Jan 17th Martin Luther King’s Birthday (no class)
Wed Jan 19th Bayesian Inference (cont'd)
Mon Jan 24th Bayesian Inference (cont'd)
Wed Jan 26th Decision making under uncertainty
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 16.
Mon Jan 31st Markov decision processes and applications in HRI
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 17.1- 17.3.
notes Slides
Wed Feb 2nd Action selection for collaboration (student presentations)
  • Cost-Based Anticipatory Action Selection for Human-Robot Fluency, Hoffman and Breazal. (Main: Jady , Con:Lucy)
  • Joint action: bodies and minds moving together, Sebanz et al.(Main: Isaac)
Mon Feb 7th Experimental Design Sample consent form notes
Wed Feb 9th Training of human teams and shared mental models (student presentations)
  • The Impact of Cross-Training on Team Effectiveness, Marks et al. (Main:Varun)
  • Planning, Shared Mental Models and Coordinated Performance: An Empirical Link is Established, Stout et al. (Main:Weizhe)
Mon Feb 14th 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 (Main:Justin, Con:Elizabeth)
  • Adaptive Coordination Strategies for Human-Robot Handovers, Huang et al. (Main:Hyunjun, Con:Neel)
Wed Feb 16th Intent inference (student presentations)
  • Goal Inference as Inverse Planning, Baker et al. (Main: Mina)
  • Planning-based Prediction for Pedestrians, Ziebart et al (Main: Libin, Con:Xinhu)
Mon Feb 21st President's Day (no class)
Wed Feb 23rd Expressiveness in robot motion (student presentations)
  • Expressing thought: improving robot readability with animation principles, Takayama et al. (Main:John, Con:Anir)
  • The Illusion of Robotic Life, Ribeiro and Paiva. (Main:Amy, Con:Ragheb)
Project Proposal Due.
Mon Feb 28th Generation of expressive motion (student presentations)
  • Generating Legible Robot Motion, Dragan and Srinivasa. (Main:Lucy, Con:Jady)
  • Enhancing Interaction Through Exaggerated Motion Synthesis, Gielniak and Thomaz. (Main:Brent, Con:Isaac)
Wed Mar 2nd Guest Lecture: Mohammad Soleymani
Mon Mar 7th Guest Lecture: Jesse Thomason
Wed Mar 9th Planning with partial observability notes Slides
Mon Mar 14th Spring Recess (no class)
Wed Mar 16th Spring Recess (no class)
Mon Mar 21st Planning with partially observable human states (student presentations)
  • Intention-aware motion planning, Bandyopadhyay et al. (Main: Seokjoo , Con: Varun )
  • Belief Space Planning for Sidekicks in Cooperative Games, Macindowe et al (Main: Elizabeth , Con: Weizhe)
Mon Mar 23rd Planning with human state dynamics (student presentations)
  • Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model, Nikolaidis et al. (Main: Neel, Con: Hyunjun)
  • Learning Latent Representations to Influence Multi-Agent Interaction, Xie et al. (Main: Xinhu, Con:Amy )
Mon Mar 28th Planning in shared autonomy domains (student presentations)
  • Shared Autonomy via Hindsight Optimization, Javdani et al. (Main: Anir, Con: John)
  • Autonomy Infused Teleoperation with Application to BCI Manipulation, Muelling et al. (Main: Ragheb, Con: Mina)
Wed Mar 30th 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)
Mon April 4th Guest Lecture: Heather Culbertson
Wed Apr 6th Active learning in HRI (student presentations)
  • Designing robot learners that ask good questions, Cakmak and Thomaz. (Main: Elizabeth )
  • Active Preference-Based Learning of Reward Functions, Sadigh et al. (Main: Neel, Con: Brent )
Mon Apr 11th Reinforcement learning with human feedback (student presentations)
  • Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning, Knox and Stone. (Main: Varun, Con:Libin)
  • Interactive Learning from Policy-Dependent Human Feedback, MacGlashan et al.(Main: Hyunjun, Con: Seokjoo )
Wed Apr 13th Integrating learning and planning in HRI (student presentations)
  • Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies, Liu et al. (Main: Anir, Con: Justin)
  • Planning with Trust for Human-Robot Collaboration, Chen et al. (Main: Justin, Con: Libin)
Mon Apr 18th Quality Diversity (student presentations)
  • Illuminating search spaces by mapping elites, Mouret and Clune (Main: Seokjoo  )      
  •  Confronting the Challenge of Quality Diversity, Pugh et al.(Main: Neel)
Wed Apr 20th Authoring Human-Robot Interactions
  • A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy, Fontaine and Nikolaidis (Main: Jady)
  • Authoring and verifying human-robot interactions, Porfirio et al (Main: Isaac)  
Mon Apr 25th Project Presentations
Wed Apr 27th Project Presentations
Wed May 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 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: