Autonomous learning and chaining of motor primitives using the Free Energy Principle
Neural and Evolutionary Computing
2020-05-12 v1 Artificial Intelligence
Machine Learning
Robotics
Abstract
In this article, we apply the Free-Energy Principle to the question of motor primitives learning. An echo-state network is used to generate motor trajectories. We combine this network with a perception module and a controller that can influence its dynamics. This new compound network permits the autonomous learning of a repertoire of motor trajectories. To evaluate the repertoires built with our method, we exploit them in a handwriting task where primitives are chained to produce long-range sequences.
Keywords
Cite
@article{arxiv.2005.05151,
title = {Autonomous learning and chaining of motor primitives using the Free Energy Principle},
author = {Louis Annabi and Alexandre Pitti and Mathias Quoy},
journal= {arXiv preprint arXiv:2005.05151},
year = {2020}
}