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R-IAC : Robust Intrinsically Motivated Active Learning

Adrien Baranes 1 Pierre-Yves Oudeyer 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : IAC was initially introduced as a developmental mechanisms 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 : Pierre Rouanet <>
Submitted on : Friday, December 4, 2009 - 10:25:35 AM
Last modification on : Wednesday, July 3, 2019 - 10:48:04 AM
Long-term archiving on: : Thursday, June 17, 2010 - 7:02:59 PM


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  • HAL Id : inria-00438595, version 1



Adrien Baranes, Pierre-Yves Oudeyer. R-IAC : Robust Intrinsically Motivated Active Learning. International Conference on Development and Learning 2009, Jun 2009, Shanghai, China. ⟨inria-00438595⟩



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