Virtual Adversarial Humans finding Hazards in Robot Workplaces

TL;DR

This paper introduces a novel hazard analysis method for robot workplaces using virtual adversarial humans in simulation to identify unsafe scenarios, aiming to improve early hazard detection and safety planning.

Contribution

It presents a new approach framing hazard analysis as a search in simulation environments with adversarial virtual humans to uncover hazards.

Findings

Successfully identified hazards in six industrial robot scenarios

Demonstrated effectiveness of adversarial virtual humans in hazard detection

Potential to reduce re-design costs by early hazard identification

Abstract

During the planning phase of industrial robot workplaces, hazard analyses are required so that potential hazards for human workers can be identified and appropriate safety measures can be implemented. Existing hazard analysis methods use human reasoning, checklists and/or abstract system models, which limit the level of detail. We propose a new approach that frames hazard analysis as a search problem in a dynamic simulation environment. Our goal is to identify workplace hazards by searching for simulation sequences that result in hazardous situations. We solve this search problem by placing virtual humans into workplace simulation models. These virtual humans act in an adversarial manner: They learn to provoke unsafe situations, and thereby uncover workplace hazards. Although this approach cannot replace a thorough hazard analysis, it can help uncover hazards that otherwise may have been overlooked, especially in early development stages. Thus, it helps to prevent costly re-designs at later development stages. For validation, we performed hazard analyses in six different example scenarios that reflect typical industrial robot workplaces.