Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng
Computer Science Dept.
Stanford University
Stanford, CA 94305
Abstract
Autonomous helicopter flight is widely regarded to be a highly challenging control
problem. This paper presents the first successful autonomous completion on a
real RC helicopter of the following four aerobatic maneuvers: forward flip and
sideways roll at low speed, tail-in funnel, and nose-in funnel. Our experimental
results significantly extend the state of the art in autonomous helicopter flight.
We used the following approach: First we had a pilot fly the helicopter to help
us find a helicopter dynamics model and a reward (cost) function. Then we used
a reinforcement learning (optimal control) algorithm to find a controller that is
optimized for the resulting model and reward function. More specifically, we used
differential dynamic programming (DDP), an extension of the linear quadratic
regulator (LQR).
link: http://www.cs.stanford.edu/%7Epabbeel/pubs/AbbeelCoatesQuigleyNg_aaorltahf_nips2006.pdf
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