A Hazard-Informed Data Pipeline for Robotics Physical Safety

TL;DR

This paper introduces a hazard-informed data pipeline that integrates classical risk engineering with machine learning to enhance robotics physical safety through formal hazard ontologies and synthetic data generation.

Contribution

It presents a novel structured safety framework combining hazard ontology, vulnerability enumeration, and synthetic data for safer robotic systems.

Findings

Framework enables safety envelope learning

Aligns risk engineering with machine learning

Facilitates hazard-aware synthetic data generation

Abstract

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.