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.

翻译：过去十年，深度学习帮助解决了机器人领域各操作难题。与此同时，工业机器人的编程方式仍以传统程序表示与接口为主导。本文从行业实践者视角出发，对这一"AI应用鸿沟"展开分析。为此，我们提出BANSAI方法（通过神经符号AI弥合AI应用鸿沟）。该方法系统性地利用神经符号AI原理，在现代工业机器人编程工作流中建立数据驱动的亚符号程序合成与优化机制。BANSAI在概念上融合了先前多条研究路线，并提出了迈向实际工业验证的路径。