BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 21 Apr 2024.
Builds on
4 of 11 references.
References · 4
- 2019A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithmscited by 136
- 2018The Limits and Potentials of Deep Learning for Roboticscited by 104
- 2018The Challenge of Crafting Intelligible Intelligencecited by 46
- 2020The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directionscited by 7
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Citing papers per year: 2025: 1.
1 paper.