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A Hazard-Informed Data Pipeline for Robotics Physical Safety

Alexei Odinokov, Rostislav Yavorskiy

TL;DR

The approach bridges classical risk engineering with modern machine learning pipelines, enabling safety envelope learning grounded in a formalized hazard ontology, based on explicit asset declaration, systematic vulnerability enumeration, and hazard-driven 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.

A Hazard-Informed Data Pipeline for Robotics Physical Safety

TL;DR

The approach bridges classical risk engineering with modern machine learning pipelines, enabling safety envelope learning grounded in a formalized hazard ontology, based on explicit asset declaration, systematic vulnerability enumeration, and hazard-driven 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.
Paper Structure (12 sections, 1 figure)

This paper contains 12 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Deterministic vs. Emergent Harm
  3. The Role of Synthetic Data in Mitigating Emergent Harm
  4. Literature Overview
  5. Robotics Physical Safety Engineering Pipeline
  6. Step 1: Asset Declaration (Protection Universe)
  7. Step 2: Exposure Modes (Vulnerability Enumeration)
  8. Step 3: Hazard Scenario Definition
  9. Step 4: Simulated Scene and Synthetic Data Generation
  10. Step 5: ML Fine-Tuning and Safety Envelope Learning
  11. Practical Example: Humanoid Robot Operating in a Kindergarten
  12. Conclusion

Figures (1)

  • Figure 1: Physical AI Safety Engineering Pipeline