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Python Library for Autonomous Exploration

Project description

# Explauto: A library to study, model and simulated autonomous exploration in virtual and robotics agents #

Explauto is a framework developed in the [inria FLOWERS]( team to provide a common interface for the implementation of active sensorimotor learning algorithm.

Explauto allows the standardized but flexible implementation of:

  • Sensorimotor active learning models (cognitive level)
  • Agent motor and sensory primitives (agent level)
  • Virtual and robotics setups (environment level)

All interfaces are written in Python to allow for fast development, easy deployment and quick scripting by non-necessary expert developers. Python is highly interoperable with other programming language such as C++, Matlab, etc … and a number of tutorial explaing how to bind the library with third-party softwares using othe programming languages are provided.

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!

## Scientific grounding ## Explauto’s scientific roots trace back from Intelligent Adaptive Curiosity algorithmic architecture (REF Kaplan Oudeyer (lien vers hal)), which has been extended to a more general family of autonomous exploration architecture by (REF Baranes) and recently expressed as a compact and unified formalism (REF Moulin-Frier Oudeyer).

## Documentation ##

The full Explauto documentation on a html format can be found [here]( It provides tutorials, examples and a complete API.

## Installation ##

Before you start building PyPot, you need to make sure that the following packages are already installed on your computer:

Once it is done, you can build and install PyPot with the classical:

cd Explauto python install

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

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