Optimized Execution of Action Chains Using Learned Performance Models of Abstract Actions (bibtex)
by Freek Stulp, Michael Beetz
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.
Reference:
Freek Stulp, Michael Beetz, "Optimized Execution of Action Chains Using Learned Performance Models of Abstract Actions", In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI), 2005.
Bibtex Entry:
@InProceedings{stulp05optimized,
  author =       {Freek Stulp and Michael Beetz},
  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},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Models, Learning, Planning, Action},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {},
  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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