Repository aiming at reproducibility of experiments and analysis of results provided in ICDL2016 conference paper.

We provide source code of the experiments (Licence GPLv3), and data analysis.

We do not provide data as it is too large, but we explain how to re-generate it.

Here is the ICDL paper.

Here is a video of the setup.

Here is a Jupyter Notebook explaining the Active Model Babbling algorithm with comparisons to other algorithms: notebook.

• notebook describing how to run the experiments

• notebook describing how to analyze the data.

• Explauto on branch random_goal_babbling, this commit
• pydmps on branch master, this commit
• Run with Numpy version '1.10.1', and scipy version '0.15.1'.