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

arXiv: https://arxiv.org/abs/2404.13652 · HTML: https://mlsanity.com/paper/2404.13652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [The Challenge of Crafting Intelligible Intelligence](https://mlsanity.com/paper/1803.04263/) (2018)
- [The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions](https://mlsanity.com/paper/2010.14537/) (2020)

## Built on by (most cited first, 1 of 1)

- [AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation](https://mlsanity.com/paper/2503.09409/) (2025)

## 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.