This course provides a high-level overview of recent research in applying AI to robotics through surveying frontier research.
This course covers topics including computer vision (robot perception), natural language processing (NLP), robotic control, imitation learning, reinforcement learning (RL), and human-robot interaction (HRI).
Students will read and present scientific research papers, write critiques, and conduct a research project to deepen their understanding of these topics.
There will be no textbook. Links to all required readings will be provided in the class schedule.
Week 1 | Introduction |
Week 2 | Robot Perception |
Week 3 | Transformers and Diffusion Models |
Week 4 | Data Quality and Curation |
Week 5 | Large Pretrained Models |
Week 6 | Reinforcement Learning (RL) and Inverse RL |
Week 7 | Representation Learning and Meta Learning |
Week 8 | Human-Robot Interaction |
Week 9 | Emerging Topics |
Week 10 | Final Project Presentations |
20% Paper Presentation
20% Paper Response
10% Class Participation
50% Final Project
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Engineering EDI Resources:
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Please see here for information.