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AGENTS
2001
Springer

Learning structured reactive navigation plans from executing MDP navigation policies

8 years 8 months ago
Learning structured reactive navigation plans from executing MDP navigation policies
Autonomous robots, such as robot office couriers, need navigation routines that support flexible task execution and effective action planning. This paper describes XFRMLEARN, a system that learns structured symbolic navigation plans. Given a navigation task, XFRMLEARN learns to structure continuous navigation behavior and represents the learned structure as compact and transparent plans. The structured plans are obtained by starting with monolithic default plans that are optimized for average performance and adding subplans to improve the navigation performance for the given task. Compactness is achieved by incorporating only subplans that achieve significant performance gains. The resulting plans support action planning and opportunistic task execution. XFRMLEARN is implemented and extensively evaluated on an autonomous mobile robot.
Michael Beetz, Thorsten Belker
Added 28 Jul 2010
Updated 28 Jul 2010
Type Conference
Year 2001
Where AGENTS
Authors Michael Beetz, Thorsten Belker
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