Open-ended evolutionary robotics: an information theoretic approach
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Learning exploration strategies in model-based reinforcement learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Intelligent adaptive curiosity (IAC) was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without preprogramming 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 robust intelligent adaptive curiosity (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. Finally, an open source accompanying software containing these algorithms as well as tools to reproduce all the experiments presented in this paper is made publicly available.