000151591 001__ 151591
000151591 005__ 20190213063704.0
000151591 037__ $$aCONF
000151591 245__ $$aA state-action neural network supervising navigation and manipulation behaviors for complex task reproduction
000151591 269__ $$a2010
000151591 260__ $$c2010
000151591 336__ $$aConference Papers
000151591 520__ $$aIn 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.
000151591 6531_ $$aNeural networks
000151591 6531_ $$aLearning from demonstration
000151591 6531_ $$aGaussian Mixture Models
000151591 700__ $$0243004$$g177386$$aD'halluin, Florent
000151591 700__ $$ade Rengervé, Antoine
000151591 700__ $$aLagarde, Matthieu
000151591 700__ $$aGaussier, Philippe
000151591 700__ $$g115671$$aBillard, Aude$$0240594
000151591 700__ $$aAndry, Pierre
000151591 7112_ $$d November, 5-7, 2010$$cÖrenäs Slott, Sweden$$aTenth International Conference on Epigenetic Robotics
000151591 909C0 $$xU10660$$0252119$$pLASA
000151591 909CO $$pconf$$pSTI$$ooai:infoscience.tind.io:151591
000151591 917Z8 $$x177386
000151591 917Z8 $$x177386
000151591 917Z8 $$x177386
000151591 937__ $$aEPFL-CONF-151591
000151591 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000151591 980__ $$aCONF