CS395T: Robot Manipulation and Learning
Adaptive Planning and Control Methods to Operate in Unstructured Environments
Course Description
The ultimate goal of a robot is to manipulate its environment: change the environment's state through interactions in order to achieve a task. Over the decades, multiple techniques have been proposed for robot manipulation, including solutions for grasping, planning, motion, and interaction control. These techniques are successful in industrial settings where robots work in controlled and known environments. However, classical solutions for robot manipulation fail to generalize to less structured domains such as human homes, offices, and campuses. How can robots manipulate successfully in these general environments? How can they become dexterous active helpers in our homes? This course provides a deep overview of the most relevant techniques in robot manipulation with a special emphasis on those based on learning and how they have recently achieved higher levels of generalization and adaptation. The course covers a) classical foundational methodologies that provide a theoretical basis and help understand the challenges of robot manipulation, and b) modern learning methodologies that overcome the manipulation challenges by learning from experiences or from imitating (human) experts. The topics covered will include methods for sensorimotor control of manipulation and manipulation planning, providing a comprehensive overview of the elements necessary to design a manipulating robot. It will include solutions based on optimization, search, control, representation, reinforcement and imitation learning.
Course Time and Location
Time: Tue/Thu 9:30 to 11amLocation: GDC 1.406
Online Platforms
Ed Discussion (TODO: change link)Canvas
Gradescope
