線虫の神経回路を再利用した小型ロボット制御
Can a Compact Neuronal Circuit Policy be Re-purposed to Learn Simple Robotic Control?
線虫C.エレガンスの神経回路構造を模した小型ニューロン回路ポリシーを探索的最適化で再目的化し、実機ローバーの自動駐車を含むロボット制御タスクで深層学習並みの性能を少ないパラメータで達成した。
著者: Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
分類: cs.LG, cs.AI, cs.NE, cs.RO, stat.ML
原文アブストラクト
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as the model of biological neural circuits reparameterized for the control of an alternative task. We learn instances of NCPs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Neuronal circuit policies perform on par and in some cases surpass the performance of contemporary deep learning models with the advantage leveraging significantly fewer learnable parameters and realizing interpretable dynamics at the cell-level.
