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Combining Declarative, Procedural and Predictive Knowledge to Generate and Execute Robot Plans Efficiently and Robustly (bibtex)
@ARTICLE{stulp_combining_2008,
  author = {Stulp, Freek and Beetz, Michael},
  title = {Combining Declarative, Procedural and Predictive Knowledge to Generate
	and Execute Robot Plans Efficiently and Robustly},
  journal = {Robotics and Autonomous Systems Journal (Special Issue on Semantic
	Knowledge)},
  year = {2008},
  abstract = {One of the main challenges in motor control is expressing high-level
	goals in terms of low-level actions. To do so effectively, motor
	control systems must reason about actions at different levels of
	abstraction. Grounding high-level plans in low-level actions is essential
	semantic knowledge for plan-based control of real robots. We present
	a robot control system that uses declarative, procedural and predictive
	to generate, execute and optimize plans. Declarative knowledge is
	represented in {PDDL}, durative actions constitute procedural knowledge,
	and predictive knowledge is learned by observing action executions.
	We demonstrate how learned predictive knowledge enables robots to
	autonomously optimize plan execution with respect to execution duration
	and robustness in real-time. The approach is evaluated in two different
	robotic domains.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr