Sao Mai Nguyen

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WE BRIDGE THE GAP BETWEEN TWO ISSUES IN INFANT DEVELOPMENT vocal development and intrinsic motivation. We propose and experimentally test the hypothesis that general mechanisms of intrinsically motivated spontaneous exploration, also called curiosity-driven learning, can self-organize developmental stages during early vocal learning. We introduce a(More)
Sao Mai Nguyen1 ∗, Pierre-Yves Oudeyer1 † 1 Flowers Team, INRIA and ENSTA ParisTech, France, 200 avenue de la Vieille Tour , 33 405 Talence Cedex, France Abstract We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and(More)
This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we(More)
This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After(More)
In this paper we address the problem of learning to recognize objects by manipulation in a developmental robotics scenario. In a life-long learning perspective, a humanoid robot should be capable of improving its knowledge of objects with active perception. Our approach stems from the cognitive development of infants, exploiting active curiosity-driven(More)
This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that(More)
The combination of learning by intrinsic motivation and social learning has been shown to improve the learner's performance and gain precision over a wider range of motor skills, with for instance the SGIM-D learning algorithm [1]. Nevertheless, this bootstrapping a-priori depends on the demonstrations made by the teacher. We propose in this paper to(More)
This paper studies an interactive learning system that couples internally guided learning and social interaction for robot learning of motor skills. We present Socially Guided Intrinsic Motivation with Interactive learning at the Meta level (SGIM-IM), an algorithm for learning forward and inverse models in high-dimensional, continuous and non-preset(More)
We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes for cumulative learning. In the present work, we consider the learning of tasks that are hierarchically(More)