Skip to Main content Skip to Navigation
Conference papers

R-IAC : Robust Intrinsically Motivated Active Learning

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.
Document type :
Conference papers
Complete list of metadata
Contributor : Pierre Rouanet Connect in order to contact the contributor
Submitted on : Friday, December 4, 2009 - 10:25:35 AM
Last modification on : Friday, March 25, 2022 - 3:34:01 PM
Long-term archiving on: : Thursday, June 17, 2010 - 7:02:59 PM


Files produced by the author(s)


  • 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⟩



Record views


Files downloads