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High Performance Execution of Everyday Pick-and-Place Tasks by Integrating Transformation Planning and Reactive Execution (bibtex)
@INPROCEEDINGS{mosenlechner_high_2008,
  author = {Mösenlechner, Lorenz and Müller, Armin and Beetz, Michael},
  title = {High Performance Execution of Everyday Pick-and-Place Tasks by Integrating
	Transformation Planning and Reactive Execution},
  booktitle = {Proceedings of the 1st International Workshop on Cognition for Technical
	Systems, München, Germany, 6-8 October},
  year = {2008},
  abstract = {We investigate the plan-based control of physically and sensorically
	realistic simulated autonomous mobile robots performing everyday
	pick-and-place tasks in human environments, such as table setting.
	Our approach applies {AI} planning techniques to transform default
	plans that can be inferred from instructions for activities of daily
	life into flexible, high-performance robot plans. To find high performance
	plans the planning system applies transformations such as carrying
	plates to the table by stacking them or leaving cabinet doors open
	while setting the table, which require substantial changes of the
	control structure of the intended activities. We argue and demonstrate
	that applying {AI} planning techniques directly to concurrent reactive
	plan languages, instead of using layered software architectures with
	different languages, enables the robot action planner to achieve
	substantial performance improvements (23\% - 45\% depending on the
	tasks). We also argue that the transformation of concurrent reactive
	plans is necessary to obtain the results. Our claims are supported
	by extensive empirical investigations in realistic simulations.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr