论文标题
使用自由能原理的自主学习和电动机链接
Autonomous learning and chaining of motor primitives using the Free Energy Principle
论文作者
论文摘要
在本文中，我们将自由能原理应用于运动原始学习问题。回声状态网络用于生成电动机轨迹。我们将该网络与感知模块和可以影响其动态的控制器结合在一起。这个新的复合网络允许自主学习运动轨迹的曲目。为了评估用我们的方法构建的曲目，我们将其在手写任务中利用它们，在该任务中，原始人被链接以产生远程序列。
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.
