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IEEE T. Autonomous Mental Development. 01/2011; 3:30-42.
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IEEE Transactions on Systems, Man, and Cybernetics, Part A. 01/2010; 40:13-28.
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From Animals to Animats 11, 11th International Conference on Simulation of Adaptive Behavior, SAB 2010, Paris - Clos Lucé, France, August 25-28, 2010. Proceedings; 01/2010
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Social Robotics - Second International Conference on Social Robotics, ICSR 2010, Singapore, November 23-24, 2010. Proceedings; 01/2010
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ABSTRACT: We are interested in understanding how babies learn to recognize facial expressions without having a teaching signal allowing to associate a facial expression to a given abstract label (i.e the name of the facial expression `sadness', `happiness'...). Our starting point was a mathematical model showing that if the baby uses a sensory motor architecture for the recognition of the facial expression then the parents must imitate the baby facial expression to allow the on-line learning. In this paper, a first series of robotics experiments showing that a simple neural network model can control the robot head and learn on-line to recognize the facial expressions (the human partner imitates the robot prototypical facial expressions) is presented. We emphasize the importance of the emotions as a mechanism to ensure the dynamical coupling between individuals allowing to learn more complex tasks.
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 18-22, 2010, Taipei, Taiwan; 01/2010
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Development of Multimodal Interfaces: Active Listening and Synchrony, Second COST 2102 International Training School, Dublin, Ireland, March 23-27, 2009, Revised Selected Papers; 01/2009
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ABSTRACT: We present a neural model for the control of an animat. The model is based on two structures. The first one enables visual
navigation using landmarks. It may be used in unknown and changing environments. The second structure enables building a proximity
map of the environment. Using this map, an animat may successfully reach different goals linked to different motivations and
solve various types of action selection problems.
10/2007: pages 319-323;
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Neural Computation. 01/2005; 17:1339-1384.
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Advances in Artificial Life, 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005, Proceedings; 01/2005
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Ad-Hoc, Mobile, and Wireless Networks, Second International Conference, ADHOC-NOW 2003 Montreal, Canada, October 8-10, 2003, Proceedings; 01/2003
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Artificial Intelligence in Medicine, 9th Conference on Artificial Intelligence in Medicine in Europe, AIME 2003, Protaras, Cyprus, October 18-22, 2003, Proceedings; 01/2003
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IJCAI-03, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, August 9-15, 2003; 01/2003
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IEEE Transactions on Systems, Man, and Cybernetics, Part A. 01/2003; 33:523-532.
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Artificial Neural Nets Problem Solving Methods, 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN2003, Maó, Menorca, Spain, June 3-6, 2003 Proceedings, Part I; 01/2003
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ABSTRACT: As models of living beings acting in a real world biorobots undergo an accelerated “philogenic” complexification. The first efficient robots performed simple animal behaviours (e.g., those of ants, crickets) and later on isolated elementary behaviours of complex beings. The increasing complexity of the tasks robots are dedicated to is matched by an increasing complexity and versatility of the architectures now supporting conditioning or even elementary planning.
Behavioral and Brain Sciences 11/2001; 24(06):1051 - 1053. · 25.06 Impact Factor
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IEEE Transactions on Systems, Man, and Cybernetics, Part A. 01/2001; 31:431-442.
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Bio-inspired Applications of Connectionism, 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13-15, 2001, Proceedings, Part II; 01/2001
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01/2000
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Recent Advances in Parallel Virtual Machine and Message Passing Interface, 7th European PVM/MPI Users' Group Meeting, Balatonfüred, Hungary, September 2000, Proceedings; 01/2000
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ABSTRACT: In this paper, we describe how a mobile robot under simple visual control can retrieve a particular goal location in an open environment. Our model neither needs a precise map nor to learn all the possible positions in the environment. The system is a neural architecture inspired by neurobiological analysis of how visual patterns named landmarks are recognized. The robot merges these visual informations and their azimuth to build a plastic representation of its location. This representation is used to learn the best movement to reach the goal. A simple and fast on-line learning of a few places located near the goal allows this goal to be reached from anywhere in its neighborhood. The system uses only a very rough representation of the robot environment and presents very high generalization capabilities. We describe an efficient implementation of autonomous and motivated navigation tested on our robot in real indoor environments. We show the limitations of the model and its possible extensions.
Robotics and Autonomous Systems. 01/2000; 30:155-180.
