This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning (RL) and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will learn the theoretical foundations for these concepts. Prerequisites: CS106A or equivalent, CME 100 or equivalent (for linear algebra), CME 106 or equivalent (for probability theory), and AA 174A/274A.
Lectures meet virtually on Mondays and Wednesdays from 1:00pm to 2:20pm. All office hours are also virtual.
Prof. Bohg's office hours are on Fridays 1:00pm to 2:00pm, and by appointment.
Prof. Pavone's office hours are on Fridays 4:00pm to 5:00pm, and by appointment.
Prof. Sadigh's office hours are on Fridays 9:00am to 10:00am, and by appointment.
CA office hours are on Tuesdays from 4:00pm to 6:00pm and Fridays from 10:00am to 12:00pm.