BANSAI：ニューロシンボリックプログラミングによる産業ロボティクスへのAI導入ギャップの橋渡し
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming
産業用ロボットの現場でAI導入が進まない「AI導入ギャップ」を分析し、ニューロシンボリックAIを活用してデータ駆動型のプログラム合成と最適化を実現するBANSAIアプローチを提案した論文。
著者: Benjamin Alt, Julia Dvorak, Darko Katic, Rainer Jäkel, Michael Beetz, Gisela Lanza
分類: cs.RO, cs.AI, cs.CE, cs.LG
原文アブストラクト
Over the past decade, deep learning helped solve manipulation problems across all domains of robotics. At the same time, industrial robots continue to be programmed overwhelmingly using traditional program representations and interfaces. This paper undertakes an analysis of this "AI adoption gap" from an industry practitioner's perspective. In response, we propose the BANSAI approach (Bridging the AI Adoption Gap via Neurosymbolic AI). It systematically leverages principles of neurosymbolic AI to establish data-driven, subsymbolic program synthesis and optimization in modern industrial robot programming workflow. BANSAI conceptually unites several lines of prior research and proposes a path toward practical, real-world validation.
