Yuchen Cui

This seminar-style course examines how robot actions and skills are represented, learned, and grounded from human data for manipulation. The course centers on major debates in action representation: low-level control versus task-level abstractions; instantaneous actions versus trajectories; structured representations versus end-to-end learning; teleoperation versus passive observation; imitation versus reinforcement learning; robot-centric versus object-centric actions; explicit versus latent skills; and how action representation shapes perception. We will read classical and state-of-the-art papers, discuss their assumptions and inductive biases, and connect these ideas to manipulation, contact-rich interaction, and humanoid embodiment.

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.

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 overview of fundamental principles in robotics, including kinematics, dynamics, perception, control, and planning. Topics also explore the integration of hardware and software in robotic systems, along with applications in real-world scenarios such as robotic manipulation. Instruction includes lectures, hands-on projects, and discussions. Designed for undergraduate students interested in robotics and its interdisciplinary connections to computer science, engineering, and artificial intelligence.

This course provides an overview of fundamental principles in robotics, including kinematics, dynamics, perception, control, and planning. Topics also explore the integration of hardware and software in robotic systems, along with applications in real-world scenarios such as robotic manipulation. Instruction includes lectures, hands-on projects, and discussions. Designed for undergraduate students interested in robotics and its interdisciplinary connections to computer science, engineering, and artificial intelligence.