Computational Human-Robot Interaction

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

Lectures: Mon / Wed 15:30 - 17:20 KAP 145

Office Hours: Mon / Wed 17:30-18:00 RTH 401 (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:

identify and discuss the different components that make up decision-theoretic reasoning (e.g., MDPs, POMDPs) and learning-based techniques (learning from demonstration, reinforcement learning) that support human-robot interaction
explain the computational and practical challenges of applying these techniques in real-world interaction settings and compare them in terms of robustness, scalability and performance
analyze the design and implementation of a user study to evaluate algorithms for HRI
critique a research paper’s methods and analysis
communicate effectively scientific research to a peer audience

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%
Participation	10%

Assessment of Assignments

Paper Presentations: Each week, students will present assigned papers in class, on a rotating schedule. There will be two presentation types: (1) Main: this will be 15 minutes long and the goal will be to provide an overview of the paper. (2) Con: in some papers we will have an additional 5 minute presentation where the goal will be to discuss technical limitations of the proposed method and ideas on how to overcome them. You will be evaluated based on your demonstrated understanding of the technical content, how you relate the paper to the lectures and previous readings, the clarity, structure and timing of the presentation and how well you answer questions.
Final Project: It should be a substantial piece of work and is expected to take between 60-80 hours over 8 weeks. The project can be a research project or an extensive in-depth, publication quality literature review(~40-50 papers, grouped based on different categorization criteria, identification of gaps in the literature). You will write a project proposal, work on the project upon approval and do a presentation near the end of the term. You are strongly encouraged to work on something related to your research and think of the project as preliminary work for a paper at a selective, peer-reviewed conference. You will be evaluated based on proposal quality, demonstrated mastery of content, novelty of contribution or categorization of previous work and identification of gaps in the case of a survey, and quality of the final project report.
Quizzes: There will be a short (10 minute) quiz on the material from the lecture one week before and the readings for the day. The quiz will frequently connect different concepts from multiple readings and/or the material. This quiz is intended to ensure that you are keeping pace with the material and are prepared for the day’s presentations, and is not meant to be onerous. I will drop your four lowest quiz grades when calculating your final grade in the course. There will be no make-up quizzes.
Participation: Students will get the most out of this class if they are active and engaged. This includes asking questions and participating in discussions. There will be explicit time for questions and discussion after each student presentation. I expect students to participate during all lecture sessions.

Important Dates:

Feb 19th: Project Proposal Submission.

Project Proposal:

Research Project.
- Identify the problem.
- Summarize previous work
- Describe key insight
- Hypothesize on the results: what do you expect to see? It is perfectly OK to be wrong :)

Research survey.
- Scope the survey: what are you going to include and what will you ignore?
- Identify categorization criteria
- Start the survey with 10 papers.

Schedule:

Day	Date	Topic	Reading	Notes
Mon	Jan 13th	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
Wed	Jan 15th	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.	code notes_A notes_B
Thu	Jan 20th	no class (holiday)
Wed	Jan 22nd	Bayesian inference (cont'd) and decision making under uncertainty	Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 16.	notes
Mon	Jan 27th	Markov decision processes and applications in HRI	Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 17.1- 17.3.	code notes
Wed	Jan 29th	Action selection for collaboration (student presentations)	Cost-Based Anticipatory Action Selection for Human-Robot Fluency, Hoffman and Breazal. (Main:Heramb, Con:Jiali) Joint action: bodies and minds moving together, Sebanz et al.(Main:Nathan)
Mon	Feb 3rd	Experimental Design	Which statistical test to choose (optional) Toward a science of robotics: Goals and standards for experimental research, Takayama. (optional)	Sample consent form notes
Wed	Feb 5th	Training of human teams and shared mental models (student presentations)	The Impact of Cross-Training on Team Effectiveness, Marks et al. (Main:Aravind) Planning, Shared Mental Models and Coordinated Performance: An Empirical Link is Established, Stout et al. (Main:Yilei)
Mon	Feb 10th	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:Hejia, Con:Rittwik) Adaptive Coordination Strategies for Human-Robot Handovers, Huang et al. (Main:Matt, Con:Shao-Hung)
Wed	Feb 12th	Intent inference (student presentations)	Goal Inference as Inverse Planning, Baker et al. (Main:Prateek) Planning-based Prediction for Pedestrians, Ziebart et al (Main:Kuang, Con:Roxanna)
Mon	Feb 17th	no class (holiday)
Wed	Feb 19th	Expressiveness in robot motion (student presentations)	Expressing thought: improving robot readability with animation principles, Takayama et al. (Main:Aniruddh, Con:Arka) The Illusion of Robotic Life, Ribeiro and Paiva. (Main:Yi, Con:Nathan)	Project Proposal Due.
Mon	Feb 24th	Generation of expressive motion (student presentations)	Generating Legible Robot Motion, Dragan and Srinivasa. (Main:Jiali, Con:Heramb) Enhancing Interaction Through Exaggerated Motion Synthesis, Gielniak and Thomaz. (Main:Rittwik, Con:Aravind)
Wed	Feb 26th	Guest Lecture: Mohammad Soleymani
Mon	Mar 2nd	Planning with partial observability	Planning and acting in partially observable stochastic domains, Kaelbling et al. "A brief introduction to Partially Observable Markov Decision Processes" (optional)	notes
Wed	Mar 4th	Planning with partially observable human states (student presentations)	Intention-aware motion planning, Bandyopadhyay et al. (Main: Shao-Hung, Con: Matt) Belief Space Planning for Sidekicks in Cooperative Games, Macindowe et al (Main: Roxanna, Con: Yilei)
Mon	Mar 9th	Planning with human state dynamics (student presentations)	Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model, Nikolaidis et al. (Main: Arka, Con: Aniruddh) Planning for Autonomous Cars that Leverage Effects on Human Actions, Sadigh et al. (Main:Heramb, Con: Jiali)
Wed	Mar 11th	Planning in shared autonomy domains (student presentations)	Shared Autonomy via Hindsight Optimization, Javdani et al. (Main: Matt, Con: Hejia) Autonomy Infused Teleoperation with Application to BCI Manipulation, Muelling et al. (Main: Nathan, Con: Yi)
Mon	Mar 16th	no class (spring recess)
Wed	Mar 18th	no class (spring recess)
Mon	Mar 23rd	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
Wed	Mar 25th	Learning from demonstration in HRI (student presentations)	Trajectories and Keyframes for Kinesthetic Teaching: A Human-Robot Interaction Perspective, Akgun et al. (Main: Yi, Con:Kuang) Confidence-based policy learning from demonstration using gaussian mixture models, Chernova and Veloso. (Main: Aravind, Con:Prateek)
Mon	Mar 30th	Active learning in HRI (student presentations)	Designing robot learners that ask good questions, Cakmak and Thomaz. (Main: Prateek, Con: Roxanna) Active Preference-Based Learning of Reward Functions, Sadigh et al. (Main: Hejia, Con: Aniruddh)
Wed	Apr 1st	Guest Lecture (Jiali Duan)		notes
Mon	Apr 6th	Guest Lecture (Nanyun Peng)
Wed	Apr 8th	Reinforcement learning with human feedback (student presentations)	Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning, Knox and Stone. (Main: Yilei, Con: Jiali) Interactive Learning from Policy-Dependent Human Feedback, MacGlashan et al. (Main: Shao-Hung, Con:Aravind)
Mon	Apr 13th	Integrating learning and planning in HRI (student presentations)	Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks, Nikolaidis et al. (Main: Aniruddh, Con: Heramb) Planning with Trust for Human-Robot Collaboration, Chen et al.(Main:Arka, Con: Prateek)
Wed	Apr 15th	Optimal teaching (student presentations)	Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications, sections 1-6, Brown and Niekum (Main: Rittwik, Con: Hejia) Algorithmic and human teaching of sequential decision tasks, Cakmak and Lopez. (Main: Roxanna, Con: Yi)
Mon	Apr 20th	Pedagogical reasoning (student presentations)	Cooperative inverse reinforcement learning, Hadfield et al. Showing versus doing: Teaching by demonstration, Ho et al.
Wed	Apr 22nd	Communication and signaling (student presentations)	ConTaCT: Deciding to Communicate during Time-Critical Collaborative Tasks in Unknown, Deterministic Domains, Unhelkar and Shah Implicit Communication in a Joint Action, Knepper et al.
Mon	Apr 27th	Project Presentation
Wed	Apr 29th	Project Presentation
Fri	May 8th			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: