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Optimized Execution of Action Chains Using Learned Performance Models of Abstract Actions (bibtex)
  author = {Stulp, Freek and Beetz, Michael},
  title = {Optimized Execution of Action Chains Using Learned Performance Models
	of Abstract Actions},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on Artificial
	Intelligence ({IJCAI)}},
  year = {2005},
  abstract = {Many plan-based autonomous robot controllers generate chains of abstract
	actions in order to achieve complex, dynamically changing, and possibly
	interacting goals. The execution of these action chains often results
	in robot behavior that shows abrupt transitions between subsequent
	actions, causing suboptimal performance. The resulting motion patterns
	are so characteristic for robots that people imitating robotic behavior
	will do so by making abrupt movements between actions. In this paper
	we propose a novel computation model for the execution of abstract
	action chains. In this computation model a robot first learns situation-specific
	performance models of abstract actions. It then uses these models
	to automatically specialize the abstract actions for their execution
	in a given action chain. This specialization results in refined chains
	that are optimized for performance. As a side effect this behavior
	optimization also appears to produce action chains with seamless
	transitions between actions.}
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Last edited 29.01.2013 17:37 by Quirin Lohr