Crowdsourcing-based Virtual Commissioning of Dynamic Human-Robot Teams (CroViCo)

点击翻译按钮获取中文摘要

Recent research on human-robot task sharing has been putting emphasis on dynamic workflows. Respective methods allow for mutual adaptation and continuous coordination of actions instead of following a fixed and rigid shared plan. Hence, they support different product assembly sequences according to worker preferences, handling of errors or interruptions because of external events. It is therefore necessary to comprehensively consider various workflows that may emerge during joint task execution when seeking to assess dynamic approaches. Benchmarking can only then provide profound insights into the benefits and limitations of a dynamic cobot system when put into concrete application scenarios. User studies as commonly used in the field of human-robot interaction are clearly too costly and time-consuming for this purpose – we, consequently, envision a novel benchmarking approach that takes inspiration from virtual commissioning techniques and simulates dynamic execution processes automatically. The challenge when trying to generate such processes lies in the scalable simulation of realistic human working strategies in cooperative handling processes. These are hard to foresee and model due to their indeterminism. In this research project, we address this scientific gap regarding cognitive digital human models: We are seeking to investigate the extent to which data on human decisions and actions can be gathered by online crowdsourcing, and how respective data can be generalized by stochastic models of working strategies that can be applied to different benchmark tasks as an artificial intelligence of simulated humans. From this point, the long-term project goal is to establish a virtual commissioning framework with an interface that renders automatic, reproducible performance measurements for numerous dynamic human-robot teaming methods feasible.