BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming

Deep neural networks and subsymbolic learning have progressed In this paper, we propose that neurosymbolic programming tremendously over the past decade, producing increasingly - a principled combination of symbolic AI and deep learning promising results in the domain of program synthesis and (DL) for program representation, synthesis and optimization robot control [1]. While the use of robots in the manufacturing - can overcome this gap. We describe BANSAI (Bridging industries is ubiquitous, the current degree of industry adoption the AI Adoption Gap via Neurosymbolic AI), an approach for of artificial intelligence-based robot program synthesis and optimization the application of neurosymbolic programming to industrial remains very limited, particularly with regard to deep robotics. To that end, we contribute an analysis of the AI adoption learning (DL) [2]. This reflects a broader phenomenon in the gap, highlighting a mismatch between the requirements manufacturing industry, where artificial intelligence (AI) adoption imposed by the industrial robot programming and deployment lags behind the academic state of the art, with a "lack of process and the exigencies of state-of-the-art AI-based manipulation, substantial evidence of industrial success" at technology readiness program synthesis and optimization approaches.