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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.06130 |
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| _version_ | 1866918375601471488 |
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| author | Odinokov, Alexei Yavorskiy, Rostislav |
| author_facet | Odinokov, Alexei Yavorskiy, Rostislav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06130 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Hazard-Informed Data Pipeline for Robotics Physical Safety Odinokov, Alexei Yavorskiy, Rostislav Robotics Artificial Intelligence 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. |
| title | A Hazard-Informed Data Pipeline for Robotics Physical Safety |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06130 |