Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines.
This course introduces nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include the nonlinear dynamics of robotic manipulators, applied optimal and robust control and motion planning. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines.
Each semester we will devote a number of lectures to taking a "deeper dive" into some new and exciting area. This spring, we will dive into learning models from data / model-based reinforcement learning.
Note: The course staff is working to make the very best of the online format this Spring. We will explore interactive and flipped lectures. Recordings will be provided for students that cannot attend synchronously.
Class Time and Location
- Dongchan Lee (TA): Monday 2-3 pm EST
- AJ Miller (TA): Wednesday 12-1 pm EST
- Eric Chen (TA): Friday 3-4 pm EST
- Assignments: 40%
- Midterm: 30%
- Final Project: 30%
- No final exam