モジュール型ニューロコントローラによる高度な運動制御
A Compositional Neuro-Controller for Advanced Motor Control Tasks
生物の運動制御のモジュール性に着想を得て、複数の運動スタイルを干渉なく逐次学習し切り替えられる構成型ニューロコントローラを提案し、ヘビ型ロボットのシミュレーションで有効性を示した。
著者: Kirill Makukhin
分類: cs.RO
原文アブストラクト
Humans and animals developed a sophisticated motor control apparatus and there is much evidence that it has a modular structure. The modularity offers a range of benefits, e.g. ability to learn dissociable motion styles without interference and forgetting, fast adaptation and de-adaptation to changes conditions. However in robotics, building a controller that can efficiently incrementally learn new motion styles and provide switching between them is a formidable challenge. In this paper we address the problem by proposing a novel biologically inspired compositional neuro-controller. We have shown that the compositional controller is able to reproduce a set of trajectories more efficiently comparing to a simple controller, exploiting incremental learning benefits. Second, we have demonstrated that the proposed controller is able to learn different locomotion styles and switch between them in a simulated robot-snake.
