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Freek Stulp and Michael Beetz. Optimized Execution of Action Chains through Subgoal Refinement. 2005. ICAPS Workshop ``Plan Execution: A Reality Check''
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Abstract
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.
BibTeX Entry
@Misc{stulp05optimizeda,
author = {Freek Stulp and Michael Beetz},
title = {Optimized Execution of Action Chains through Subgoal Refinement},
year = {2005},
note = {ICAPS Workshop ``Plan Execution: A Reality Check''},
url = {http://ic.arc.nasa.gov/people/sailesh/icaps2005wksp/},
bib2html_pubtype = {Refereed Workshop Paper},
bib2html_rescat = {Plan-based Robot Control, Robot Learning},
bib2html_groups = {IAS, AGILO},
bib2html_keywords = {},
abstract = {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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