Technical University of Munich

@inproceedings{beetz_learning_2001,
 author = {M Beetz and T Belker},
 title = {Learning Structured Reactive Navigation Plans from Executing {MDP}
	policies},
 booktitle = {Proceedings of the 5th International Conference on Autonomous Agents},
 year = {2001},
 pages = {19–20},
 abstract = {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.},
}