AI Bibliography |
Abbeel, P., Coates, A., Quigley, M., & Ng, A. (2006). An application of reinforcement learning to aerobatic helicopter flight. Advances in Neural Information Processing Systems, 19. |
Resource type: Journal Article BibTeX citation key: Abbeel2006 View all bibliographic details |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Decision Theory, General Subcategories: Autonomous systems, Deep learning, Human decisionmaking, Machine learning Creators: Abbeel, Coates, Ng, Quigley Publisher: Collection: Advances in Neural Information Processing Systems |
Attachments |
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).
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