学習ベースのタスク非依存スパイキング中枢パターン生成器
Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach
スパイキングニューラルネットワークを用い、学習によって構築するタスク非依存かつ生物学的に妥当な中枢パターン生成器の枠組みを提案し、ロボット実験で異なる速度の安定歩行と歩行周期内の速度変化を実現した。
著者: Elie Aljalbout, Florian Walter, Florian Röhrbein, Alois Knoll
分類: cs.NE, cs.LG, cs.RO, stat.ML
原文アブストラクト
Legged locomotion is a challenging task in the field of robotics but a rather simple one in nature. This motivates the use of biological methodologies as solutions to this problem. Central pattern generators are neural networks that are thought to be responsible for locomotion in humans and some animal species. As for robotics, many attempts were made to reproduce such systems and use them for a similar goal. One interesting design model is based on spiking neural networks. This model is the main focus of this work, as its contribution is not limited to engineering but also applicable to neuroscience. This paper introduces a new general framework for building central pattern generators that are task-independent, biologically plausible, and rely on learning methods. The abilities and properties of the presented approach are not only evaluated in simulation but also in a robotic experiment. The results are very promising as the used robot was able to perform stable walking at different speeds and to change speed within the same gait cycle.
