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Python Library for Autonomous Exploration

Project description

# 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 provide a common interface for the implementation of active sensorimotor learning algorithm.

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)

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](

## Documentation ##

### 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]]( We strongly recommend to read this [short introduction]( into developmental robotics before going through the tutorials.

### Tutorials ###

Most of Explauto’s documentation is written as [IPython notebooks]( If you do not know how to use them, please refer to the [dedicated section](

### API ###

Explauto’s API can be found on a html format [here](

## Installation ##

The best way to install Explauto at the moment is to clone the repo and used it in [development mode]( It is also available as a [python package]( The core of explauto depends on the following packages:

For more details, please refer to the [installation section]( of the documentation.

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