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Conference papers

Robust Intrinsically Motivated Exploration and Active Learning

Abstract : IAC was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without pre-programming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in unprepared sensorimotor spaces with large unlearnable subspaces. Then, we introduce a novel formulation of IAC, called R-IAC, and show that its performances as an intrinsically motivated active learning algorithm are far superior to IAC in a complex sensorimotor space where only a small subspace is neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme.
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Contributor : Jérôme Béchu Connect in order to contact the contributor
Submitted on : Monday, September 28, 2009 - 3:43:19 PM
Last modification on : Friday, March 25, 2022 - 3:34:01 PM


  • HAL Id : inria-00420306, version 1



Adrien Baranes, Pierre-yves Oudeyer. Robust Intrinsically Motivated Exploration and Active Learning. IEEE International Conference on Learning and Development, 2009, Shangaï, China. ⟨inria-00420306⟩



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