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Transformational Planning for Mobile Manipulation based on Action-related Places
Andreas Fedrizzi, Lorenz Moesenlechner, Freek Stulp, and Michael Beetz. Transformational Planning for Mobile Manipulation based on Action-related Places. In International Conference on Advanced Robotics (ICAR), 2009.
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Abstract
Opportunities for interleaving or parallelizing actions are abundant in everyday activities. Being able to perceive, predict and exploit such opportunities leads to more efficient and robust behavior. In this paper, we present a mobile manipulation platform that exploits such opportunities to optimize its behavior, e.g. grasping two objects from one location simultaneously, rather than navigating to two different locations. To do so, it uses a general least-commitment representation of place, called \arplace, from which manipulation is predicted to be successful. Models for \arplaces are learned from experience using Support Vector Machines and Point Distribution Models, and take into account the robot's morphology and skill repertoire. We present a transformational planner that reasons about \arplaces, and applies transformation rules to its plans if more robust and efficient behavior is predicted.
BibTeX
@InProceedings{fedrizzi09transformational,
  title                    = {Transformational Planning for Mobile Manipulation based on Action-related Places},
  author                   = {Andreas Fedrizzi and Lorenz Moesenlechner and Freek Stulp and Michael Beetz},
  booktitle                = {International Conference on Advanced Robotics (ICAR)},
  year                     = {2009},
  abstract                 = {Opportunities for interleaving or parallelizing actions are abundant in everyday activities. Being able to perceive, predict and exploit such opportunities leads to more efficient and robust behavior. In this paper, we present a mobile manipulation platform that exploits such opportunities to optimize its behavior, e.g. grasping two objects from one location simultaneously, rather than navigating to two different locations. To do so, it uses a general least-commitment representation of place, called \arplace, from which manipulation is predicted to be successful. Models for \arplace{}s are learned from experience using Support Vector Machines and Point Distribution Models, and take into account the robot's morphology and skill repertoire. We present a transformational planner that reasons about \arplace{}s, and applies transformation rules to its plans if more robust and efficient behavior is predicted.},
  bib2html_pubtype         = {Refereed Conference Paper},
  bib2html_rescat          = {Action-Related Places for Mobile Manipulation},
  url                      = {http://www9.cs.tum.edu/publications/pdf/fedrizzi09transformational.pdf}
}

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