Intrinsically Motivated Exploration for Developmental and Active Sensorimotor Learning

  • Pierre-Yves Oudeyer
  • Adrien Baranes
  • Frédéric Kaplan
Part of the Studies in Computational Intelligence book series (SCI, volume 264)


Intrinsic motivation is a central mechanism that guides spontaneous exploration and learning in humans. It fosters incremental and progressive sensorimotor and cognitive development by pushing exploration of activities of intermediate complexity given the current state of capabilities. This chapter presents and studies two computational intrinsic motivation systems that share similarities with human intrinsic motivation systems, IAC and R-IAC, that aim at self-organizing and efficiently guiding exploration for sensorimotor learning in robots. IAC was initially introduced to model the qualitative formation of developmental motor stages of increasing complexity, as shown in the Playground Experiment which we will outline. In this chapter, we argue that IAC and other intrinsically motivated learning heuristics could also 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 “interesting”, i.e. neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme. Finally, an open-source accompanying software containing these algorithms as well as tools to reproduce all the experiments in simulation presented in this paper is made publicly available.

Index Terms

active learning intrinsically motivated learning exploration developmental robotics artificial curiosity sensorimotor learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pierre-Yves Oudeyer
    • 1
  • Adrien Baranes
    • 1
  • Frédéric Kaplan
    • 2
  1. 1.INRIAFrance
  2. 2.CRAFT-EPFLSwitzerland

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