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Automatic Design of Robot Behaviors through Constraint Networks Acquisition

Mathias Paulin 1 Christian Bessière 1 Jean Sallantin 1
1 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Control architectures, such as the LAAS architecture [1], CLARATY [12] and HARPIC [9], have been developped to provide autonomy to robots. To achieve a robot's task, these control architectures plan sequences of sensorimotor behaviors. Currently carried out by roboticians, the design of sensorimotor behaviors is a truly complex task that can require many hours of hard work and intensive computations. In this paper, we propose a Constraint Programming-based framework to interact with roboticians during the sensori-motor behaviors design. A constraint network acquisition platform and a CSP-Based planner are used to automatically design sensorimotor behaviors. Moreover, our architecture exploits the propagation properties of the acquired CSPs to supervise the execution of a given sensorimotor behavior. Some experimental results are presented to validate our approach.
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Mathias Paulin, Christian Bessière, Jean Sallantin. Automatic Design of Robot Behaviors through Constraint Networks Acquisition. 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2008, Dayton, OH, United States. pp.275-282, ⟨10.1109/ICTAI.2008.83⟩. ⟨lirmm-00349025⟩

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