Parameterized maneuver learning for autonomous helicopter flight

Many robotic control tasks involve complex dynamics that are hard to model. Hand-specifying trajectories that satisfy a system's dynamics can be very time-consuming and often exceedingly difficult. We present an algorithm for automatically generating large classes of trajectories for difficult control tasks by learning parameterized versions of desired maneuvers from multiple expert demonstrations. Our algorithm has enabled the successful execution of several parameterized aerobatic maneuvers by our autonomous helicopter.