Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 29, Issue 13, Pages 1608-1639Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364910371999
Keywords
Apprenticeship learning; autonomous flight; autonomous helicopter; helicopter aerobatics; learning from demonstration
Categories
Funding
- DARPA [FA8650-05-C-7261]
- Stanford Graduate Fellowship
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Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopter's capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks being demonstrated by an expert. These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics. Our experimental results include the first autonomous execution of a wide range of maneuvers, including but not limited to in-place flips, in-place rolls, loops and hurricanes, and even auto-rotation landings, chaos and tic-tocs, which only exceptional human pilots can perform. Our results also include complete airshows, which require autonomous transitions between many of these maneuvers. Our controllers perform as well as, and often even better than, our expert pilot.
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