A Theoretical Foundation for Erroneous Behavior in Human–Robot Interaction

The advent of mass customization has precipitated a need within the industry for the implementation of collaborative robots, which facilitate the integration of human cognitive capabilities with the speed and repeatability of robots. This coupling, however, engenders a closer collaboration between the partners, thereby necessitating collective synergy to achieve optimal scheduling while circumventing musculoskeletal disorders. It is imperative to study and analyze the behavior of humans and robots in interaction, as the current paradigm strives to achieve an optimal interaction between the two partners with the objective of ensuring productivity, safety, cognitive ergonomics and preventing musculoskeletal disorders. However, human behavior is variable and can, on occasion, give rise to anomalies in the interaction. Consequently, it is imperative that the robot partner exhibits precise behavior, whether proactive or reactive. This paper puts forth a unified perspective on robot behavior when confronted with human abnormal behavior during interaction on the factory floor. This systematic literature review and meta-analysis employs the PRISMA methodology to examine the literature on human and robot behavior in human–robot interaction in an industrial context, with a particular focus on robot behavior when confronted with human abnormal behavior during interaction. A systematic search of nearly 2,609 papers yielded 133 for inclusion in this systematic review. In light of the findings presented in this review, it can be concluded that the selection of robot actions based on human behavior represents a novel area of research that requires further investigation, particularly with regard to proactive online behavioral approaches. Indeed, there is a vast array of robot behavior modalities in response to typical human behavior (e.g., command input). However, there is currently no prescribed robot reaction based on atypical human behavior (e.g., misplacement in the factory floor, repetition of tasks, etc.). This lack of definition complicates the deployment of such technology in the smart factory. Consequently, it is essential to define new decision strategies based, for instance, on artificial intelligence approaches.