The goal of our research is to build machines which exploit their
natural dynamics to achieve extraordinary agility and efficiency. We
believe that this challenge involves a tight coupling between
mechanical design and underactuated nonlinear control, and that tools
from machine learning and optimal control can be used to produce this
coupling when classical control techniques fail. Our projects include
minimally-actuated dynamic walking on moderate terrain, quadrupedal
locomotion on extreme terrain, fixed-wing acrobatics, flapping-winged
flight, and feedback control for fluid dynamics.