自由エネルギー原理による運動プリミティブの自律学習と連鎖
Autonomous learning and chaining of motor primitives using the Free Energy Principle
エコーステートネットワークと知覚・制御モジュールを組み合わせ、自由エネルギー原理に基づいて運動軌道のレパートリーを自律的に学習し、手書きタスクでプリミティブを連鎖させて長い系列を生成できることを示した論文。
著者: Louis Annabi, Alexandre Pitti, Mathias Quoy
分類: cs.NE, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
