# How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach

arXiv: https://arxiv.org/abs/2412.11387 · HTML: https://mlsanity.com/paper/2412.11387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning](https://mlsanity.com/paper/2212.05711/) (2022)
- [Masked Language Model Scoring](https://mlsanity.com/paper/1910.14659/) (2019)

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

- [SafePlan: Leveraging Formal Logic and Chain-of-Thought Reasoning for Enhanced Safety in LLM-based Robotic Task Planning](https://mlsanity.com/paper/2503.06892/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, deploying LLM-generated code in robotic systems without safety verification poses significant risks. This paper outlines a safety layer that verifies the code generated by ChatGPT before executing it to control a drone in a simulated environment. The safety layer consists of a fine-tuned GPT-4o model using Few-Shot learning, supported by knowledge graph prompting (KGP). Our approach improves the safety and compliance of robotic actions, ensuring that they adhere to the regulations of drone operations.