Error-related potentials detection to enhance human-robot collaboration: a mini review

Error-related potentials (ErrPs) have been studied to evaluate wrong decisions or actions in several contexts. An ErrP is an electrical potential on the scalp generated by the perception of errors and occurs unwittingly. In human-robot collaboration (HRC), ErrP detection can be used to trigger a feedback or an action to adapt the system to the user. This contributes to the improvement of HRC, taking into account user performance. However, to our knowledge, the detection of ErrPs in HRC has not been widely explored, resulting in only a few studies. This systematic review will present work on ErrP-based interfaces related to adaptation, control, and neuroergonomics for HRC. Thirteen articles were included after the exclusion criteria of the review stages. The average accuracy of ErrP detection was between 54 and 87.2%. In most cases, the authors simulated the occurrence of unexpected behavior of the robot. The robot mistakes occurred randomly between 20 and 35% of the total trials. Some works focused on the robot learning process and adaptation between humans and robots. The mental model and the robot behavior policy were updated based on the decoded ErrPs during collaborative interactions. Control-related works have included ErrPs detection/features as input inside the control loop or algorithm. Other studies assessed the influence of mental workload variability in the adaptation process, given that a high mental workload affects the cognitive processes needed to perceive errors. Thus, ErrPs present advantages for enhancing HRC, and this review opens the way to further developments in the robotic domain.