Computational Human-Robot Interaction

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

Lectures: Mon / Wed 14:00 - 15:50 (online)

TA: Heramb Nemlekar

Office Hours: Wed 15:50-16:50 (online)

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.

Grading:

Component Percentage
Paper Presentations 30%
Final Project 40%
Weekly Quizzes 20%
Homework 10%

Assessment of Assignments

Important Dates:

March 1st: Project Proposal Submission.

Project Proposal:

Schedule:

Day Date Topic Reading Notes
Wed Jan 20th 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)
Slides
Mon Jan 25th 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.
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notes_A notes_B Slides
Wed Jan 27th Bayesin Inference (cont'd)
Mon Feb 1st Decision making under uncertainty
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 16.
notes
Wed Feb 3rd Markov decision processes and applications in HRI
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 17.1- 17.3.
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notes Slides
Mon Feb 8th Action selection for collaboration (student presentations)
  • Cost-Based Anticipatory Action Selection for Human-Robot Fluency, Hoffman and Breazal. (Main: Sourish , Con: Kushal)
  • Joint action: bodies and minds moving together, Sebanz et al.(Main: Yang)
Wed Feb 10th Experimental Design Sample consent form notes
Mon Feb 15th President's Day (no class)
Wed Feb 17th Training of human teams and shared mental models (student presentations)
  • The Impact of Cross-Training on Team Effectiveness, Marks et al. (Main:Jun)
  • Planning, Shared Mental Models and Coordinated Performance: An Empirical Link is Established, Stout et al. (Main:John)
Mon Feb 22nd 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:Sophie, Con:Pegah)
  • Adaptive Coordination Strategies for Human-Robot Handovers, Huang et al. (Main:Lingchen, Con:Iris)
Wed Feb 24th Intent inference (student presentations)
  • Goal Inference as Inverse Planning, Baker et al. (Main: Sanjana Sambur)
  • Planning-based Prediction for Pedestrians, Ziebart et al (Main: Nathaniel Sands, Con:Zhonghao Shi)
Mon March 1st Expressiveness in robot motion (student presentations)
  • Expressing thought: improving robot readability with animation principles, Takayama et al. (Main:Kegan Strawn, Con:Bingjie Tang)
  • The Illusion of Robotic Life, Ribeiro and Paiva. (Main:Hanyuan Xiao, Con:Bryon Tjanaka)
Project Proposal Due.
Wed March 3rd Generation of expressive motion (student presentations)
  • Generating Legible Robot Motion, Dragan and Srinivasa. (Main: Hanchen Xie, Con:Connie Zhang)
  • Enhancing Interaction Through Exaggerated Motion Synthesis, Gielniak and Thomaz. (Main:Jiahui Zhang, Con:Yulun Zhang)
Mon March 8th Guest Lecture: TBD
Wed Mar 10th Planning with partial observability notes
Mon Mar 15th Planning with partially observable human states (student presentations)
  • Intention-aware motion planning, Bandyopadhyay et al. (Main: Kushal, Con: Sourish)
  • Belief Space Planning for Sidekicks in Cooperative Games, Macindowe et al (Main: Pegah, Con: Yang)
Wed Mar 17th Planning with human state dynamics (student presentations)
  • Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model, Nikolaidis et al. (Main: Iris, Con: Jun)
  • Learning Latent Representations to Influence Multi-Agent Interaction, Xie et al. (Main: Zhonghao , Con: John)
Mon Mar 22nd Planning in shared autonomy domains (student presentations)
  • Shared Autonomy via Hindsight Optimization, Javdani et al. (Main: Bryon, Con: Sophie)
  • Autonomy Infused Teleoperation with Application to BCI Manipulation, Muelling et al. (Main: Bingjie, Con: Lingchen)
Wed Mar 24th 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)
notes
Mon Mar 29th Guest Lecture: Prof. Jesse Thomason
Wed Mar 31th Guest Lecture: Prof. Heather Culbertson
Mon Apr 5th Active learning in HRI (student presentations)
  • Designing robot learners that ask good questions, Cakmak and Thomaz. (Main: Connie, Con: Sanjana)
  • Active Preference-Based Learning of Reward Functions, Sadigh et al. (Main: Yulun, Con: Nathaniel)
Wed Apr 7th Wellness Day (no class)
Mon Apr 12th Reinforcement learning with human feedback (student presentations)
  • Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning, Knox and Stone. (Main: Sanjana, Con:Kegan)
  • Interactive Learning from Policy-Dependent Human Feedback, MacGlashan et al.(Main: Lingchen, Con: Hanyuan)
Wed Apr 14th Integrating learning and planning in HRI (student presentations)
  • Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies, Liu et al. (Main: Iris, Con: Hanchen)
  • Planning with Trust for Human-Robot Collaboration, Chen et al. (Main: Sophie, Con: Jiahui)
Mon Apr 19th Quality Diversity (student presentations)
  • Illuminating search spaces by mapping elites, Mouret and Clune (Main: Yulun  )      
  •  Confronting the Challenge of Quality Diversity, Pugh et al.(Main: Zhonghao)
  •  Quality-Diversity Optimization: a novel branch of stochastic optimization, Chatzilygeroudis et al. (optional)
  •  Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space, Fontaine et al.(optional)
Wed Apr 21st Authoring Human-Robot Interactions
  • A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy, Fontaine and Nikolaidis (Main: Bryon)
  • Authoring and verifying human-robot interactions, Porfirio et al (Main: Sourish)  
Mon Apr 26th Project Presentation
Wed Apr 28th Project Presentation
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: