Autonomous Robot Controllers Capable of Acquiring Repertoires of Complex Skills (bibtex)
by M Beetz, F Stulp, A Kirsch, A Müller and S Buck
Abstract:
Due to the complexity and sophistication of the skills needed in real world tasks, the development of autonomous robot controllers requires an ever increasing application of learning techniques. To date, however, learning steps are mainly executed in isolation and only the learned code pieces become part of the controller. This approach has several drawbacks: the learning steps themselves are undocumented and not executable. In this paper, we extend an existing control language with constructs for specifying control tasks, process models, learning problems, exploration strategies, etc. Using these constructs, the learning problems can be represented explicitly and transparently and, as they are part of the overall program implementation, become executable. With the extended language we rationally reconstruct large parts of the action selection module of the AGILO2001 autonomous soccer robots.
Reference:
Autonomous Robot Controllers Capable of Acquiring Repertoires of Complex Skills (M Beetz, F Stulp, A Kirsch, A Müller and S Buck), In RoboCup International Symposium 2003, 2003. 
Bibtex Entry:
@inproceedings{beetz_autonomous_2003,
 author = {M Beetz and F Stulp and A Kirsch and A Müller and S Buck},
 title = {Autonomous Robot Controllers Capable of Acquiring Repertoires of
	Complex Skills},
 booktitle = {{RoboCup} International Symposium 2003},
 year = {2003},
 series = {Padova},
 month = {jul},
 abstract = {Due to the complexity and sophistication of the skills needed in real
	world tasks, the development of autonomous robot controllers requires
	an ever increasing application of learning techniques. To date, however,
	learning steps are mainly executed in isolation and only the learned
	code pieces become part of the controller. This approach has several
	drawbacks: the learning steps themselves are undocumented and not
	executable. In this paper, we extend an existing control language
	with constructs for specifying control tasks, process models, learning
	problems, exploration strategies, etc. Using these constructs, the
	learning problems can be represented explicitly and transparently
	and, as they are part of the overall program implementation, become
	executable. With the extended language we rationally reconstruct
	large parts of the action selection module of the {AGILO2001} autonomous
	soccer robots.},
}
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Autonomous Robot Controllers Capable of Acquiring Repertoires of Complex Skills (bibtex)
Autonomous Robot Controllers Capable of Acquiring Repertoires of Complex Skills (bibtex)
by M Beetz, F Stulp, A Kirsch, A Müller and S Buck
Abstract:
Due to the complexity and sophistication of the skills needed in real world tasks, the development of autonomous robot controllers requires an ever increasing application of learning techniques. To date, however, learning steps are mainly executed in isolation and only the learned code pieces become part of the controller. This approach has several drawbacks: the learning steps themselves are undocumented and not executable. In this paper, we extend an existing control language with constructs for specifying control tasks, process models, learning problems, exploration strategies, etc. Using these constructs, the learning problems can be represented explicitly and transparently and, as they are part of the overall program implementation, become executable. With the extended language we rationally reconstruct large parts of the action selection module of the AGILO2001 autonomous soccer robots.
Reference:
Autonomous Robot Controllers Capable of Acquiring Repertoires of Complex Skills (M Beetz, F Stulp, A Kirsch, A Müller and S Buck), In RoboCup International Symposium 2003, 2003. 
Bibtex Entry:
@inproceedings{beetz_autonomous_2003,
 author = {M Beetz and F Stulp and A Kirsch and A Müller and S Buck},
 title = {Autonomous Robot Controllers Capable of Acquiring Repertoires of
	Complex Skills},
 booktitle = {{RoboCup} International Symposium 2003},
 year = {2003},
 series = {Padova},
 month = {jul},
 abstract = {Due to the complexity and sophistication of the skills needed in real
	world tasks, the development of autonomous robot controllers requires
	an ever increasing application of learning techniques. To date, however,
	learning steps are mainly executed in isolation and only the learned
	code pieces become part of the controller. This approach has several
	drawbacks: the learning steps themselves are undocumented and not
	executable. In this paper, we extend an existing control language
	with constructs for specifying control tasks, process models, learning
	problems, exploration strategies, etc. Using these constructs, the
	learning problems can be represented explicitly and transparently
	and, as they are part of the overall program implementation, become
	executable. With the extended language we rationally reconstruct
	large parts of the action selection module of the {AGILO2001} autonomous
	soccer robots.},
}
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