Prerequisite Knowledge of Learning Environments in Human-Robot Collaboration for Dyadic Teams

Human-robot teams are organizational units consisting of a human and a robot working on the same task at the same time in a common workspace. They work flexibly with each other and the assignment of subtasks occurs during direct collaboration. It is known from human-human interaction that these collaborative activities require certain mental models that can also be applied to human-robot interactions (HRI). Inadequate mental models of the robot can lead to misinterpretations and thus to errors. In this work, it is proposed to use the construction of three essential mental models as prerequisite knowledge for flexible human-robot interaction. The further elaboration of these models in an industrial assembly scenario serves as a concept for the development of a prototype of a learning application in further research.