# A Hazard-Informed Data Pipeline for Robotics Physical Safety

arXiv: https://arxiv.org/abs/2603.06130 · HTML: https://mlsanity.com/paper/2603.06130/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [Safety Aware Task Planning via Large Language Models in Robotics](https://mlsanity.com/paper/2503.15707/) (2025)
- [Can AI Perceive Physical Danger and Intervene?](https://mlsanity.com/paper/2509.21651/) (2025)

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

- [Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes](https://mlsanity.com/paper/2608.14481/) (2026)

## Abstract (arXiv)

This report presents a structured Robotics Physical Safety Framework based on explicit asset declaration, systematic vulnerability enumeration, and hazard-driven synthetic data generation. The approach bridges classical risk engineering with modern machine learning pipelines, enabling safety envelope learning grounded in a formalized hazard ontology. The key contribution of this framework is the alignment between classical safety engineering, digital twin simulation, synthetic data generation, and machine learning model training.