A Hazard-Informed Data Pipeline for Robotics Physical Safety
This framework addresses the problem of ensuring physical safety in robotics for developers by formalizing hazard ontology and aligning safety engineering with ML pipelines.
This paper proposes a Robotics Physical Safety Framework that integrates classical risk engineering with machine learning pipelines to enable safety envelope learning. It achieves this by using explicit asset declaration, vulnerability enumeration, and hazard-driven synthetic data generation.
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.