Autonomous exploration, active learning and human guidance with open-source Poppy humanoid robot platform and Explauto library
Résumé
Our demonstration presents an open-source hardware and software platform which allows non-roboticists
researchers to conduct machine learning experiments to benchmark algorithms for autonomous exploration
and active learning. In particular, in addition to showing the general properties of the platform such as
its modularity and usability, we will demonstrate the online functioning of a particular algorithm which
allows efficient learning of multiple forward and inverse models and can leverage information from human
guidance. A first aspect of our demonstration is to illustrate the ease of use of the 3D printed low-cost
Poppy humanoid robotic platform, that allows non-roboticists to quickly set up and program robotic
experiments. A second aspect is to show how the Explauto library allows systematic comparison and
evaluation of active learning and exploration algorithms in sensorimotor spaces, through a Python API to
select already implemented exploration algorithms. The third idea is to showcase Active Model Babbling,
an efficient exploration algorithm dynamically choosing which task/goal space to explore and particular
goals to reach, and integrating social guidance from humans in real time to drive exploration towards
particular objects or actions.
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[Lapeyre et al., 2014] Lapeyre, M., Rouanet, P., Grizou, J., Nguyen, S., Depraetre, F., Le Falher, A., and Oudeyer,
P.-Y. (2014). Poppy Project: Open-Source Fabrication of 3D Printed Humanoid Robot for Science, Education
and Art. In Digital Intelligence 2014, page 6, Nantes, France.
[Moulin-Frier et al., 2014] Moulin-Frier, C., Rouanet, P., Oudeyer, P.-Y., and others (2014). Explauto: an open-
source Python library to study autonomous exploration in developmental robotics. In ICDL-Epirob-International
Conference on Development and Learning, Epirob.
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