Seamless Execution of Action Sequences (bibtex)
by Freek Stulp, Wolfram Koska, Alexis Maldonado, Michael Beetz
Abstract:
One of the most notable and recognizable features of robot motion is the abrupt transitions between actions in action sequences. In contrast, humans and animals perform sequences of actions efficiently, and with seamless transitions between subsequent actions. This smoothness is not a goal in itself, but a side-effect of the evolutionary optimization of other performance measures. In this paper, we argue that such jagged motion is an inevitable consequence of the way human designers and planners reason about abstract actions. We then present subgoal refinement, a procedure that optimizes action sequences. Subgoal refinement determines action parameters that are not relevant to why the action was selected, and optimizes these parameters with respect to expected execution performance. This performance is computed using action models, which are learned from observed experience. We integrate subgoal refinement in an existing planning system, and demonstrate how requiring optimal performance causes smooth motion in three robotic domains.
Reference:
Freek Stulp, Wolfram Koska, Alexis Maldonado, Michael Beetz, "Seamless Execution of Action Sequences", In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 3687-3692, 2007.
Bibtex Entry:
@InProceedings{stulp07seamless,
  author =       {Freek Stulp and Wolfram Koska and Alexis Maldonado and Michael Beetz},
  title =        {Seamless Execution of Action Sequences},
  booktitle =    {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year =         {2007},
  pages =        {3687-3692},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Models, Learning, Planning, Action},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {},
  abstract = {
One of the most notable and recognizable features of robot motion is the abrupt
transitions between actions in action sequences. In contrast, humans and animals
perform sequences of actions efficiently, and with seamless transitions between
subsequent actions. This smoothness is not a goal in itself, but a side-effect
of the evolutionary optimization of other performance measures.


In this paper, we argue that such jagged motion is an inevitable consequence of
the way human designers and planners reason about abstract actions. We then
present subgoal refinement, a procedure that optimizes action sequences. Subgoal
refinement determines action parameters that are not relevant to why the action
was selected, and optimizes these parameters with respect to expected execution
performance. This performance is computed using action models, which are learned
from observed experience. We integrate subgoal refinement in an existing
planning system, and demonstrate how requiring optimal performance causes smooth
motion in three robotic domains.
}
}
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