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

Benjamin Alt, Julia Dvorak, Darko Katic, Rainer Jäkel, Michael Beetz +1 more
4/21/2024
cs.ROcs.AIcs.CE

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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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Cite this paper

@article{alt2024bansai,
  title  = {BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming},
  author = {Benjamin Alt and Julia Dvorak and Darko Katic and Rainer Jäkel and Michael Beetz and Gisela Lanza},
  year   = {2024},
  eprint = {2404.13652},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.13652v1}
}

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