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Freek Stulp, Wolfram Koska, Alexis Maldonado, and Michael Beetz. Seamless Execution of Action Sequences. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2007.
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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.
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},
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Robotics - Motor Control},
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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