Explauto: A library to study, model and simulate curiosity-driven learning and exploration in virtual and robotic agents
Explauto is a framework developed in the Inria FLOWERS research team which provides a common interface for the implementation of active and online sensorimotor learning algorithms. It was created and is maintained by Clément Moulin-Frier and Pierre Rouanet.
Explauto provides a high-level API for an easy definition of:
- Virtual and robotics setups (Environment level)
- Sensorimotor learning iterative models (Sensorimotor level)
- Active choice of sensorimotor experiments (Interest level)
The library is open-source and freely available on Github. It is crossed-platform and has been tested on Linux, Windows and Mac OS. Do not hesitate to contact us if you want to get involved! It has been released under the GPLv3 license.
Scientific grounding
Explauto’s scientific roots trace back from Intelligent Adaptive Curiosity algorithmic architecture [Oudeyer, 2007], which has been extended to a more general family of autonomous exploration architecture by [Baranes, 2013] and recently expressed as a compact and unified formalism [Moulin-Frier, 2013].
The underlying principles are described in the following short paper:
Moulin-Frier, C.; Rouanet, P. & Oudeyer, P.-Y. Explauto: an open-source Python library to study autonomous exploration in developmental robotics International Conference on Development and Learning, ICDL/Epirob, Genova, Italy, 2014