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Combining Learning and Programming for High-Performance Robot Controllers

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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https://hal.archives-ouvertes.fr/hal-01329007
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Alexandra Kirsch, Michael Beetz. Combining Learning and Programming for High-Performance Robot Controllers. Tagungsband Autonome Mobile Systeme, 2005, Stuttgart, France. ⟨10.1007/3-540-30292-1_39⟩. ⟨hal-01329007⟩

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