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

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” fr...

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Bibliographic Details
Published in:Procedia CIRP 2024, Vol.130, p.532-537
Main Authors: Alt, Benjamin, Dvorak, Julia, Katic, Darko, Jäkel, Rainer, Beetz, Michael, Lanza, Gisela
Format: Article
Language:English
Subjects:
Industrial Robotics
Neurosymbolic Artificial Intelligence
Optimization
Program Synthesis
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Summary: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.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2024.10.125