Publications Freek Stulp


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Optimized Execution of Action Chains Using Learned Performance Models of Abstract Actions
Freek Stulp and Michael Beetz. Optimized Execution of Action Chains Using Learned Performance Models of Abstract Actions. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), 2005.
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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.
BibTeX
@InProceedings{stulp05optimized,
  title                    = {Optimized Execution of Action Chains Using Learned Performance Models of Abstract Actions},
  author                   = {Freek Stulp and Michael Beetz},
  booktitle                = {Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005)},
  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.},
  bib2html_pubtype         = {Refereed Conference Paper},
  bib2html_rescat          = {Optimizing the Execution of Symbolic Robot Plans},
  file                     = {0793.pdf:http\://ijcai.org/papers/0793.pdf:PDF}
}

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