D'halluin, Florentde Rengervé, AntoineLagarde, MatthieuGaussier, PhilippeBillard, AudeAndry, Pierre2010-09-132010-09-132010-09-132010https://infoscience.epfl.ch/handle/20.500.14299/53576In this abstract, we combine work from [Lagarde et al., 2010] and [Calinon et al., 2009] for learning and reproduction of, respectively, navigation tasks on a mobile robot and gestures with a robot arm. Both approaches build a sensory motor map under human guidance to learn the desired behavior. With several actions possible at the same time, the selec- tion of action becomes a real issue. Several solutions exist to this problem : hi- erarchical architecture, parallel modules including subsumption architectures or even a mix of both [Bryson, 2000]. In navigation, a temporal sequence learner or a state-action association learner [Lagarde et al., 2010] enables to learn a sequence of direc- tions in order to follow a trajectory. These solu- tions can be extended to action sequence learning. In this paper we propose a simple architecture based on perception-action that is able to produce complex behaviors from the incremental learning of simple tasks. Then we discuss advantages and limitations of this architecture, that raises many questions.Neural networksLearning from demonstrationGaussian Mixture Modelstext::conference output::conference paper not in proceedings