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Sonia Chernova and Manuela Veloso. Learning and Using Models of Kicking Motions for Legged Robots. In Proceedings of International Conference on Robotics and Automation (ICRA'04), New Orleans, May 2004.
Legged robots, such as the Sony AIBO, create opportunity to design rich motions to be executed in specificsituations. In particular, teams involved in robot soccer RoboCup competitions have developed many different motions forkicking the ball. Designing effective motions and determining their effects is a challenging problem that istraditionally approached through a generate and test methodology. In this paper, we present a method we developed forlearning the effects of kicking motions. Our procedure acquires models of the kicks in terms of key values that describetheir effects on the ball's trajectory, namely the angle and the distance reached. The successful automated acquisition ofthe models of different kicks is then followed by the incorporation of these models into the behaviors to select the mostpromising kick in a given state of the world. Using the robot soccer domain, we demonstrate that a robot that takes intoaccount the learned predicted effects of its actions performs significantly better than its counterpart.
@Inproceedings(Chernova04icra,
Author="Sonia Chernova and Manuela Veloso",
Title="Learning and Using Models of Kicking Motions for Legged Robots",
BookTitle="Proceedings of International Conference on Robotics and Automation (ICRA'04)",
place="New Orleans", month="May", year="2004",
abstract={Legged robots, such as the Sony AIBO, create opportunity to design rich motions to be executed in specific
situations. In particular, teams involved in robot soccer RoboCup competitions have developed many different motions for
kicking the ball. Designing effective motions and determining their effects is a challenging problem that is
traditionally approached through a generate and test methodology. In this paper, we present a method we developed for
learning the effects of kicking motions. Our procedure acquires models of the kicks in terms of key values that describe
their effects on the ball's trajectory, namely the angle and the distance reached. The successful automated acquisition of
the models of different kicks is then followed by the incorporation of these models into the behaviors to select the most
promising kick in a given state of the world. Using the robot soccer domain, we demonstrate that a robot that takes into
account the learned predicted effects of its actions performs significantly better than its counterpart.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {RoboCup Legged Soccer, RoboCup Publications}
bib2html_dl_pdf = {http://www.cs.cmu.edu/~soniac/files/ChernovaVelosoICRA04.pdf},
)
Generated by bib2html.pl (written by Patrick Riley ) on Tue Oct 09, 2007 00:00:13
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