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

TL;DR

This paper introduces BANSAI, a neurosymbolic AI approach designed to bridge the gap between advanced AI techniques and traditional industrial robot programming, aiming for practical adoption.

Contribution

It proposes a novel neurosymbolic framework that integrates data-driven program synthesis into industrial robotics workflows, addressing the AI adoption barrier.

Findings

Conceptual unification of neurosymbolic AI with industrial robotics

Framework for data-driven program synthesis in robotics

Path toward real-world validation of AI in industry

Abstract

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.