Intrinsically Motivated Multi-Task Reinforcement Learning With Open-Source Explauto Library and Poppy Humanoid Robot
Résumé
This 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, it demonstrates 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 the 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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