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.

💡 Research Summary

The paper proposes a comprehensive, hazard‑informed data pipeline that bridges classical robotics safety engineering with modern machine‑learning workflows to address both deterministic and emergent physical risks in robotic systems. It begins by critiquing the limitations of traditional safety approaches, which focus on isolated component failures, and argues that the growing complexity of “Physical AI”—multi‑agent, adaptive robots operating alongside humans—creates new systemic hazards that cannot be captured by deterministic models alone.

The authors introduce a five‑step engineering pipeline.

1. Asset Declaration (Protection Universe). All assets that must be protected—human bodies, cognitive and psychological well‑being, organizational hardware, data, and environmental resources—are exhaustively enumerated without early filtering. This step aligns with ISO 12100 and ISO 10218 standards and creates a formal “protection universe” that serves as the foundation for subsequent analysis.

2. Exposure Modes (Vulnerability Enumeration). For each declared asset, a taxonomy of possible exposure modes is built (e.g., a human arm exposed to moving actuators, a battery exposed to overheating, data exposed to corruption). This stage abstracts the ways assets can become vulnerable, independent of any specific cause, and produces a structured vulnerability map.

3. Hazard Scenario Definition. Exposure modes are concretized into causal hazard scenarios by linking them to specific failure chains (e.g., sensor occlusion → missed detection → collision). The result is a library of testable, cause‑effect scenarios that can be used both in traditional Failure Mode and Effects Analysis (FMEA) and in simulation‑based validation.

4. Simulated Scene and Synthetic Data Generation. For every hazard scenario, a high‑fidelity digital twin of the robot, workspace, and assets is constructed. The identified failure mode is programmatically injected, and controlled variations (lighting, camera viewpoint, physical parameters) are automatically generated to produce thousands of annotated scenes. This is not generic data augmentation; it is a scenario‑driven synthetic data generation process grounded in the hazard ontology, designed to expose machine‑learning models to rare or extreme events that would be impractical to capture in the real world.

5. Machine‑Learning Model Fine‑Tuning. Pre‑trained perception or control models are fine‑tuned on the synthetic datasets with safety‑oriented labels (e.g., “potential deadlock,” “overheat precursor”). The synthetic data imbues the models with an inductive bias toward recognizing subtle precursors of unsafe states while preserving their general perception capabilities.

The paper situates this pipeline within a broader literature landscape. It reviews multidimensional safety frameworks that incorporate physical, social‑psychological, cyber‑physical, temporal, and societal dimensions (Martinetti et al.), highlights the scarcity of research on physical attacks against IoT devices (Yang et al.), and surveys AI safety concerns ranging from data quality to reward hacking (Salhab et al.). It also discusses hybrid risk‑assessment methods that combine PFMEA, HAZOP, and Fault Tree Analysis with AI‑driven anomaly detection (Jalali et al.), and presents recent advances such as the SAFER framework for LLM‑based robot planning (Khan et al.) and the ASIMO V‑2.0 benchmark for evaluating physical safety understanding in foundation models (Jindal et al.).

A key insight is that the hazard ontology provides a reusable, formal representation of “what must be protected and how it can be harmed,” enabling systematic generation of synthetic data that directly targets emergent hazards. Moreover, the pipeline is designed to be iterative: real‑world incident logs can be fed back into the ontology, enriching the synthetic data pool and continuously improving model safety performance.

In conclusion, the authors deliver a clear, reproducible methodology that integrates risk‑engineering rigor with data‑driven learning. By explicitly declaring assets, enumerating exposure modes, defining concrete hazard scenarios, and generating ontology‑guided synthetic data for model fine‑tuning, the pipeline offers a practical path to embed safety envelopes into modern robotic AI systems, ensuring they can operate safely even in complex, dynamic, multi‑agent environments.