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Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.

This study deals with the Human Reliability Analysis. A causal methodology is used to report the dynamic responses over time of humans at work. This structure is mounted inside a main framework which includes multi criteria decisions about main and subsidiaries concepts that influence the work-stress and the operations management. Factors and variables from the Human, Team, Work, Environment behavior are considered and declined in constant, measurable and depended elements with inter and intra level connections. Contextualization in a safety based approach is reported as per different tasks in a motor company. Calibration on humans and assembly tasks is reported under a dynamic simulation across levels, auxiliary variables and constants. Critical influencing factors for human reliability at work are identified and modelled.