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

• URL: http://arxiv.org/abs/2404.13652v1
• Date: Sun, 21 Apr 2024 13:04:58 GMT
• Title: BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming
• Authors: Benjamin Alt, Julia Dvorak, Darko Katic, Rainer Jäkel, Michael Beetz, Gisela Lanza,
• Abstract summary: 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. BANSAI conceptually unites several lines of prior research and proposes a path toward practical, real-world validation.
• Score: 6.502950223731164
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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