Computational Human-Robot Interaction

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

Lectures: Mon / Wed 08:00 - 09:50 (VHE 206)

TA: Ya-Chuan (Sophie) Hsu

Office Hours: 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:

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 10%
Participation 10%
Homework 10%

Assessment of Assignments

Important Dates:

March 1st: Project Proposal Submission.

Project Proposal:

Schedule:

Day Date Topic Reading Notes
Mon Jan 8th What is Computational HRI?
  • Computational Human-Robot Interaction, Thomaz, Hoffman and Cakmak. (Optional)
  • Inferring Human Intent and Predicting Human Action in Human–Robot Collaboration , Hoffman et al. (Optional)
  • The Grand Challenges in Socially Assistive Robotics, Tapus et al. (Optional)
Slides
Wed Jan 10th 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
Mon Jan 15th Martin Luther King’s Birthday (no class)
Wed Jan 17th Bayesian Inference (cont'd)
Mon Jan 22th Bayesian Inference (cont'd)
Wed Jan 24th Decision making under uncertainty
  • Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 16.
notes
Mon Jan 29th Experimental Design Sample consent form notes Slides
Wed Jan 31st Action selection for collaboration (student presentations)
  • Cost-Based Anticipatory Action Selection for Human-Robot Fluency, Hoffman and Breazal. (Main: Patrick , Con: Pavel)
  • Joint action: bodies and minds moving together, Sebanz et al.(Main: Ziwei)
Mon Feb 5th Action selection for collaboration II (student presentations)
  • Coordination With Humans Via Strategy Matching., Zhao et al.. (Main: Jade , Con: Saeed)
  • Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics, Jain and Argall (Main: Zhehui , Con: Yigit)
Wed Feb 7th Training of human teams and shared mental models (student presentations)
  • The Impact of Cross-Training on Team Effectiveness, Marks et al. (Main: Shipeng)
  • Planning, Shared Mental Models and Coordinated Performance: An Empirical Link is Established, Stout et al. (Main:Nima)
Mon Feb 12th 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: Bosheng, Con:Lina)
  • Adaptive Coordination Strategies for Human-Robot Handovers, Huang et al. (Main:Xiye, Con:Shihan)
Wed Feb 14th Guest Lecture: Erdem Biyik
Mon Feb 19th President's Day (no class)
Wed Feb 21st Intent Inference (Student Presentations)
  • Goal Inference as Inverse Planning, Baker et al. (Main: Shuqin)
  • Planning-based Prediction for Pedestrians, Ziebart et al (Main: Pavel , Con:Patrick)
Mon Feb 26th Expressiveness in robot motion (student presentations)
  • Expressing thought: improving robot readability with animation principles, Takayama et al. (Main:Saeed, Con:Ziwei)
  • The Illusion of Robotic Life, Ribeiro and Paiva. (Main: Yigit, Con:Jade)
Wed Feb 28th Generation of expressive motion (student presentations)
  • Generating Legible Robot Motion, Dragan and Srinivasa. (Main:Lina, Con:Zhehui)
  • Enhancing Interaction Through Exaggerated Motion Synthesis, Gielniak and Thomaz. (Main:Shihan, Con:Shipeng)
Mon Mar 4th Planning with partial observability Project Proposal Due notes Slides
Wed Mar 6th Planning with partially observable human states (student presentations)
  • Intention-aware motion planning, Bandyopadhyay et al. (Main:  Patrick , Con: Nima )
  • Belief Space Planning for Sidekicks in Cooperative Games, Macindowe et al (Main: Pavel, Con: Bosheng)
Mon Mar 11th Spring Recess (no class)
Wed Mar 13th Spring Recess (no class)
Mon Mar 18th Planning with human state dynamics (student presentations)
  • Towards Modeling and Influencing the Dynamics of Human Learning, Tian et al. (Main: Ziwei , Con: Xiye)
  • Learning Latent Representations to Influence Multi-Agent Interaction, Xie et al. (Main: Saeed , Con: Shuqin)
Wed Mar 20th Planning in shared autonomy domains (student presentations)
  • Shared Autonomy via Hindsight Optimization, Javdani et al. (Main: Zhehui, Con: Shuqin)
  • Autonomy Infused Teleoperation with Application to BCI Manipulation, Muelling et al. (Main: Yigit , Con: Shihan )
Mon Mar 25th Integrating Learning and Planning in HRI
  • Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies, Tian et al. (Main: Shipeng, Con: Xiye)
  • Planning with Trust for Human-Robot Collaboration, Xie et al. (Main: Nima , Con: Lina)
Wed Mar 27th Language for HRI (student presentations)
  • Generative Expressive Robot Behaviors Using Large Language Models, Mahadevan et al. (Main: Boshen, Con: Patrick)
  • Sketching Robot Programs On the Fly, Porfirio et al. (Main: Lina , Con: Pavel)
Mon Apr 1st Guest Lecture (TBD)
Mon Apr 3rd Guest Lecture (TBD)
Mon Apr 8th Quality Diversity (student presentations)
  • Illuminating search spaces by mapping elites, Mouret and Clune (Main: Xiye  )      
  •  Confronting the Challenge of Quality Diversity, Pugh et al.(Main: Shihan)
Wed Apr 10th Authoring Human-Robot Interactions
  • A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy, Fontaine and Nikolaidis (Main: Shuqin , Con:Ziwei )
  • RCare World: A Human-centric Simulation World for Caregiving Robots, Ye et al (Main: ,Con: Saeed )  
Mon Apr 15th
Wed Apr 17th
Mon Apr 22nd Project Presentations
Wed Apr 24th Project Presentations
Wed May 1st 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: