In this course, we will introduce concepts from a broad spectrum of motion control and motion planning approaches involving optimization and machine learning. The course materials consist of both lecture notes and seminal papers from the robotics literature. The specific robotic systems in which we will focus are manipulators and legged systems.
The class will begin with lectures on the fundamentals of recent research topics. Throughout the semester, each student will work on their chosen research projects that they present at the end of the semester.
This class is designed for graduate students with a basic understanding of robot kinematics/dynamics, optimization, and machine learning. Experience with one of the robotic simulators is highly recommended. If you are not sure, feel free to contact me.
Assignments (30%)
Research paper presentations (30%)
Literature Presentation (10%)
Proposal Presentation (10%)
Final Presentation (10%)
Project (40%)
Proposal (5%)
Literature review (5%)
First D
Peer review (10%)
Baseline implementations (10%)
New implementations (5%)
Final paper (20%)
Trajectory optimization, mixed-integer optimization, linear complementarity constraints
Imitation learning
Legged robots
Recent topics: transfer learning, meta-learning, etc