Optimized Execution of Action Chains through Subgoal Refinement (bibtex)
by Freek Stulp, Michael Beetz
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.
Reference:
Freek Stulp, Michael Beetz, "Optimized Execution of Action Chains through Subgoal Refinement", ICAPS Workshop ``Plan Execution: A Reality Check'', 2005. ICAPS Workshop ``Plan Execution: A Reality Check''
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   = {Models, Learning, Planning, Action},
  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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