Combining Learning and Programming for High-Performance Robot Controllers (bibtex)
by Alexandra Kirsch, Michael Beetz
Abstract:
The implementation of high-performance robot controllers for complex control tasks such as playing autonomous robot soccer is tedious, error-prone, and a never ending programming task. In this paper we propose programmers to write autonomous controllers that optimize and automatically adapt themselves to changing circumstances of task execution using explicit perception, dynamics and action models. To this end we develop ROLL (Robot Learning Language), a control language allowing for model-based robot programming. ROLL provides language constructs for specifying executable code pieces of how to learn and update these models. We are currently using ROLL's mechanisms for implementing a rational reconstruction of our soccer robot controllers.
Reference:
Alexandra Kirsch, Michael Beetz, "Combining Learning and Programming for High-Performance Robot Controllers", In Tagungsband Autonome Mobile Systeme 2005, Springer Verlag, 2005.
Bibtex Entry:
@inproceedings{kirsch05combining,
  author    = {Alexandra Kirsch and Michael Beetz},
  title     = {Combining Learning and Programming for High-Performance Robot Controllers},
  booktitle = {Tagungsband Autonome Mobile Systeme 2005},
  series    = {Reihe Informatik aktuell},
  publisher = {Springer Verlag},
  year      = {2005},
  bib2html_pubtype = {Conference Paper},
  bib2html_rescat  = {Learning,Planning},
  bib2html_groups  = {Cogito,AGILO},
  abstract = {The implementation of high-performance robot controllers for complex
              control tasks such as playing autonomous robot soccer is tedious,
              error-prone, and a never ending programming task. In this paper we
              propose programmers to write autonomous controllers that optimize and
              automatically adapt themselves to changing circumstances of task execution
              using explicit perception, dynamics and action models.
              To this end we develop ROLL (Robot Learning Language), a control language
              allowing for model-based robot programming. ROLL provides language constructs
              for specifying executable code pieces of how to learn and update these
              models.  We are currently using ROLL's mechanisms for implementing a rational
              reconstruction of our soccer robot controllers.}
}
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