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. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are 

  • Reinforcement Learning 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.

You will learn the theoretical foundations for these concepts and implement them on simulated mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities.

Please note that this course is cross listed with AA274B.

Prerequisites

CS106A or equivalent, CME 100 or equivalent (for linear algebra), CME 106 or equivalent (for probability theory), and AA 171/274A.

Notes

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.