logo EDITE Pierre LUCE-VAYRAC
Identité
Pierre LUCE-VAYRAC
État académique
Thèse en cours...
Sujet: Apprentissage interactif de représentations sensori-motrices
Direction de thèse:
Laboratoire:
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
oai:hal.archives-ouvertes.fr:hal-01391427
Discovering and Manipulating Affordances
International audience
Reasoning jointly on perception and action requires to interpret the scene in terms of the agent's own potential capabilities. We propose a Bayesian architecture for learning sensorimotor representations from the interaction between perception, action, and salient changes generated by robot actions. This connects these three elements in a common representation: affordances. In this paper, we are working towards a richer representation and formalization of affordances. Current experimental analysis shows the qualitative and quantitative aspects of affordances. In addition, our formalization motivates several experiments for exploring hypothetical operations between learned affordances. In particular, we infer affordances of composite objects, based on prior knowledge on the affordances of the elementary objects.
International Symposium on Experimental Robotics (ISER 2016) https://hal.archives-ouvertes.fr/hal-01391427 International Symposium on Experimental Robotics (ISER 2016), Oct 2016, Tokyo, Japan. 2016, 2016 International Symposium on Experimental Robotics. <http://www.iser2016.org/> http://www.iser2016.org/ARRAY(0x7f5472aa0e28) 2016-10-03
oai:hal.archives-ouvertes.fr:hal-01392823
Discovering Affordances Through Perception and Manipulation
International audience
Considering perception as an observation process only is the very reason for which robotic perception methods are to date unable to provide a general capacity of scene understanding. Related work in neuroscience has shown that there is a strong relationship between perception and action. We believe that considering perception in relation to action requires to interpret the scene in terms of the agent's own potential capabilities. In this paper, we propose a Bayesian approach for learning sensorimotor representations through the interaction between action and observation capabilities. We represent the notion of affordance as a probabilistic relation between three elements: objects, actions and effects. Experiments for affordances discovery were performed on a real robotic platform in an unsupervised way assuming a limited set of innate capabilities. Results show dependency relations that connect the three elements in a common frame: affordances. The increasing number of interactions and observations results in a Bayesian network that captures the relationships between them. The learned representation can be used for prediction tasks.
The 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016) https://hal.archives-ouvertes.fr/hal-01392823 The 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Oct 2016, Daejeon, South Korea. <http://www.iros2016.org/> http://www.iros2016.org/ARRAY(0x7f547203d640) 2016-10-09