This study aims to introduce an information design focused on alleviating the workload of workers involved in human-robot collaboration. When collaborating with cooking robots in food and beverage establishments, the human worker's responsibilities ex...
This study aims to introduce an information design focused on alleviating the workload of workers involved in human-robot collaboration. When collaborating with cooking robots in food and beverage establishments, the human worker's responsibilities extend beyond assigned task execution. They are tasked with monitoring various system parameters, such as available resources in the inventory and the maximum wait time of the ordered customer list. Based on this contextual information, which includes aspects like food production rates and potentially halting orders to prevent excessive wait times, workers determine subsequent actions. We've noticed that the need to multitask in these settings requires workers to constantly pay attention to understand the situation fully. To capture this, we identified performance factors that occur when human operators and cooking robots collaborate. By employing hierarchical task analysis, we identified requirements that could minimize workers' multitasking in these settings. Following this, we proposed an information design, the effectiveness of which was validated through in-depth interviews with experienced workers in the food and beverage sector. In the food and beverage domain, there is a distinct requirement for environmental information that reduces the cognitive workload of workers. Such information aids better comprehension of system status, including cooking robots and their ambient context. We proposed an information design encompassing three key functionalities: 1) action guides, 2) status checks, and 3) control functions. Action guides facilitate task instruction and execution, status checks aid the identification of the system’s environmental conditions, and control functions enable prompt responses when abnormal conditions occur. We have developed an information design specifically intended to reduce the multitasking-induced workload in human-robot collaborative environments. This design has the potential to mitigate the collaborative burden between cooking robots and humans, enhancing task efficiency by providing work instructions, performance guidance, and alert information for abnormal conditions.