Yuchen Cui

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.

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.