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Transformational Planning for Autonomous Household Robots using Libraries of Robust and Flexible Plans (bibtex)
  author = {Müller, Armin},
  title = {Transformational Planning for Autonomous Household Robots using Libraries
	of Robust and Flexible Plans},
  school = {Technische Universität München},
  year = {2008},
  abstract = {One of the oldest dreams of Artificial Intelligence is the realization
	of autonomous robots that achieve a level of problem-solving competency
	comparable to humans. Human problem-solving capabilities are particularly
	impressive in the context of everyday ac- tivities such as performing
	household chores: people are able to deal with ambiguous and incomplete
	information, they adapt their plans to different environments and
	specific sit- uations achieving intuitively almost optimal behavior,
	they cope with interruptions and failures and manage multiple interfering
	jobs. The investigations presented in this work make substantial
	progress in the direction of building robots that show similar behavior.
	This thesis addresses the problem of competently accomplishing everyday
	manipu- lation activities, such as setting the table and preparing
	meals, as a plan-based control problem. In plan-based control, robots
	do not only execute their programs but also reason about and modify
	them. We propose {TRANER} (Transformational Planner) as a suitable
	planning system for the optimization of everyday manipulation activities.
	{TRANER} real- izes planning through a generate-test cycle in which
	plan revision rules propose alternative plans and new plans are simulated
	in order to test and evaluate them. The unique features of {TRANER}
	are that it can realize very general and abstract plan revisions
	such as "stack objects before carrying them instead of handling them
	one by one" and that it successfully operates on plans in a way that
	they generate reliable, flexible, and efficient robot behavior in
	realistic simulations. The key contributions of this dissertation
	are threefold. First, it extends the plan rep- resentation to support
	the specification of robust and transformable plans. Second, it pro-
	poses a library of general and flexible plans for a household robot,
	using the extended plan representation. Third, it establishes a powerful,
	yet intuitive syntax for transforma- tion rules together with a set
	of general transformation rules for optimizing pick-and-place tasks
	in an everyday setting using the rule language. The viability and
	strength of the approach is empirically demonstrated in comprehen-
	sive and extensive experiments in a simulation environment with realistically
	simulated action and sensing mechanisms. The experiments show that
	transformational planning is necessary to tailor the robot's activities
	and that it is capable of substantially improving the robot's performance.},
  url = {}
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Last edited 29.01.2013 17:37 by Quirin Lohr