A Hazard-Informed Data Pipeline for Robotics Physical Safety
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.
| Authors | Alexei Odinokov, Rostislav Yavorskiy |
|---|---|
| Primary category | cs.RO |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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