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Freek Stulp and Michael Beetz. Refining the execution of abstract actions with learned action models. Journal of Artificial Intelligence Research (JAIR), 32, June 2008.

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Abstract

Robots reason about abstract actions, such as <i>go to position `l'</i>, in order to decide what to do or to generate plans for their intended course of action. The use of abstract actions enables robots to employ small action libraries, which reduces the search space for decision making. When executing the actions, however, the robot must tailor the abstract actions to the specific task and situation context at hand. In this article we propose a novel robot action execution system that learns success and performance models for possible specializations of abstract actions. At execution time, the robot uses these models to optimize the execution of abstract actions to the respective task contexts. The robot can so use abstract actions for efficient reasoning, without compromising the performance of action execution. We show the impact of our action execution model in three robotic domains and on two kinds of action execution problems: (1) the instantiation of free action parameters to optimize the expected performance of action sequences; (2) the automatic introduction of additional subgoals to make action sequences more reliable.

BibTeX Entry

@Article{stulp08refining,
  author =            {Freek Stulp and Michael Beetz},
  title =             {Refining the execution of abstract actions with learned action models},
  journal =           {Journal of Artificial Intelligence Research (JAIR)},
  year =              {2008},
  volume =            {32}
  month =             {June},
  bib2html_pubtype =  {Journal},
  bib2html_rescat =   {Robotics - Motor Control},
  abstract =          {
  Robots reason about abstract actions, such as \emph{go to position
    `l'}, in order to decide what to do or to generate plans for their
  intended course of action. The use of abstract actions enables
  robots to employ small action libraries, which reduces the search space
  for decision making. When executing the actions, however, the robot
  must tailor the abstract actions to the specific task and situation
  context at hand.
  In this article we propose a novel robot action execution system
  that learns success and performance models for possible
  specializations of abstract actions. At execution time, the robot uses
  these models to optimize the execution of abstract actions
  to the respective task contexts. The robot can so use abstract
  actions for efficient reasoning, without compromising the 
  performance of action execution. We show the impact of our action
  execution model in three robotic domains and on two kinds of
  action execution problems: (1) the instantiation of free action
  parameters to optimize the expected performance of action sequences;
  (2) the automatic introduction of additional subgoals to make action
  sequences more reliable.
  }
}

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