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 and complete programming and written assignments 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 | Large Language Models |
| Week 4 | Diffusion Models |
| Week 5 | Reinforcement Learning & Inverse RL |
| Week 6 | Representation Learning |
| Week 7 | Human-Robot Interaction |
| Week 8 | Data Quality |
| Week 9 | Robotics + X |
| Week 10 | Final Project Presentations |
20% Paper Presentation
20% Paper Response
10% Class Participation
50% Final Project
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Engineering EDI Resources:
There are a number of specific resources on Equity, Diversity, and Inclusion available to students in the Samueli School of Engineering, including trained faculty officers in each department who can be consulted if you have a question on EDI issues and are not sure where else to turn.
Please see here for information.