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? |
|
Slides |
Mon | Jan 25th | Probability and Bayesian inference |
|
code notes_A notes_B Slides |
Wed | Jan 27th | Bayesin Inference (cont'd) | ||
Mon | Feb 1st | Decision making under uncertainty |
|
notes |
Wed | Feb 3rd | Markov decision processes and applications in HRI |
|
code notes Slides |
Mon | Feb 8th | Action selection for collaboration (student presentations) |
|
|
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) |
| |
Mon | Feb 22nd | Action coordination in human-robot teams (student presentations) |
| |
Wed | Feb 24th | Intent inference (student presentations) |
|
|
Mon | March 1st | Expressiveness in robot motion (student presentations) |
|
Project Proposal Due. |
Wed | March 3rd | Generation of expressive motion (student presentations) |
| |
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) |
|
|
Wed | Mar 17th | Planning with human state dynamics (student presentations) |
|
|
Mon | Mar 22nd | Planning in shared autonomy domains (student presentations) |
|
|
Wed | Mar 24th | Learning techniques for HRI |
|
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) |
|
|
Wed | Apr 7th | Wellness Day (no class) | ||
Mon | Apr 12th | Reinforcement learning with human feedback (student presentations) |
|
|
Wed | Apr 14th | Integrating learning and planning in HRI (student presentations) |
|
|
Mon | Apr 19th | Quality Diversity (student presentations) |
|
|
Wed | Apr 21st | Authoring Human-Robot Interactions |
|
|
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: