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Integration of Programming and Learning in a Control Language for Autonomous Robots Performing Everyday Activities (bibtex)
@PHDTHESIS{kirsch_integration_2008,
  author = {Kirsch, Alexandra},
  title = {Integration of Programming and Learning in a Control Language for
	Autonomous Robots Performing Everyday Activities},
  school = {Technische Universität München},
  year = {2008},
  abstract = {Robots performing complex tasks in changing, everyday environments
	and required to improve with experience must continually monitor
	the way they execute their routines and revise them if necessary.
	Existing approaches, which use either monolithic or isolated, nonrecurring
	learning processes, cannot sufficiently focus their learning processes
	to satisfy these requirements. To meet this challenge we propose
	to make learning an integral part of the control program by providing
	a control language that includes constructs for specifying and executing
	learning problems. Our Robot Learning Language ({RoLL)} makes learning
	tasks executable within the control program. It allows for the specification
	of complete learning processes including the acquisition of experience,
	the execution of learning algorithms and the integration of learning
	results into the program. {RoLL} is built upon the concept of experience,
	which is a learning task specific symbolic summary of a problem solving
	episode. This means that experiences do not only record the observed
	data, but also include the robot's intentions and the perceived execution
	context. The experience acquisition in {RoLL} is designed in a way
	that experiences can be defined outside the primary control program,
	using hybrid automata as a tool for declaratively specifying experience
	and anchoring it to the program. The rich experience concept enables
	convenient abstraction and an economic use of experiences. {RoLL's}
	design allows the inclusion of arbitrary experience-based learning
	algorithms. Upon the completion of the learning process {RoLL} automatically
	integrates the learned function into the control program without
	interrupting Program Execution. {RoLL} enables the plug-and-play
	addition of new learning problems and keeps the control program modular
	and transparent. {RoLL's} control structures make learning an integral
	part of the control program and can serve as a powerful implementational
	platform for comprehensive learning approaches such as developmental,
	life-long and imitation learning.},
  url = {http://mediatum2.ub.tum.de/node?id=625553}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr