Course Description

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. 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, as well as imitation learning and human intent inference. 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.

Instructors

Prof. Jeannette Bohg	Prof. Marco Pavone	Prof. Dorsa Sadigh

Course Assistants

Aditya Dutt	Suneel Belkhale	Chris Agia	Roger Dai

Meeting Times

Lectures meet on Mondays and Wednesdays from 1:30pm to 2:50pm at Skilling Auditorium.

Prof. Bohg's office hours are Wednesdays, 9:00am - 10:00am in Gates 244 and on Zoom.
Prof. Pavone's office hours are Tuesdays 1:00pm - 2:00pm in Durand 261.
Prof. Sadigh's office hours are Fridays 9:00am - 10:00am in Gates 246 or by appointment.
CA office hours are (starting Week 2):

• Mondays from 2:30pm to 4:00pm (Suneel, Zoom)
• Mondays from 4:15pm to 5:30pm (Chris, Gates 100)
• Tuesdays from 9:00am to 10:30am (Roger, Huang Basement)
• Tuesdays from 4:30pm to 6:00pm (Aditya, Gates 100)
• Thursdays from 2:30pm to 4:00pm (Suneel, Zoom)
• Fridays 2:30pm to 4:00pm (Aditya, Gates 100)

Syllabus

The class syllabus can be found here.

Project Report

For students taking the course for 4 units, you are required to complete a project report by the end of the quarter. Project report guidelines can be found here.

Schedule

Subject to change. Lecture recordings will be posted on Canvas.

Blue: learning-based control and perception
Red: interaction with the physical environment
Green: interaction with humans