This course provides an investigation of recent developments in robot learning.
Particular emphasis on interactive learning approaches that enable robots to improve their performance through engagement with humans and their environment.
Critical reading and presentation of current research literature. Examination of algorithms and frameworks that support learning from demonstrations, instructions, feedback, and continuous interaction.
Study structured around student-led discussions of peer-reviewed publications.
Each student presents multiple papers, contributes to discussions and completes research project.
Goal is for students to develop deep understanding of current challenges and methodologies in interactive robot learning; and to foster ability to critically evaluate and communicate complex technical material.
There will be no textbook. Links to all required readings will be provided in the class schedule.
| Week 1 | Robot Learning Foundations |
| Week 2 | Task and Motion Planning |
| Week 3 | Representation Learning |
| Week 4 | Reinforcement Learning & Reward Design, Behavior Cloning |
| Week 5 | Shared Autonomy & Learning from Human Data |
| Week 6 | Communication & Project Elevator Pitches |
| Week 7 | Multi-Modal Interaction |
| Week 8 | Scaling Interactive Learning & Lifelong/Continual Robot Learning |
| Week 9 | Lecture (TBD) & Break |
| Week 10 | Final Project Presentations |
20% Paper Presentation
20% Paper Quizzes
60% Final Project
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