An Interactive Robot-based Vocational Assessment Game using Lego Assembly

Lego construction task paradigms are utilized in order to develop logico-mathematical abilities through visuospatial memory. This study aims to assess the relationship between cognition and performance in a simulated industrial environment by employing humanoid robots to assess the stated metrics. This system proposes to develop a smart vocational assessment and intervention service system that assesses a worker's needs for training and rehabilitation in an experimental setup that simulates a factory. The proposed approach collects and analyzes multi-sensing data and recommends personalized interventions that can improve the performance of and individual worker. In our implementation, Aldebaran's NAO robot gradually learns the features and thresholds needed to construct a decision tree that gradually learns the expected Lego model by interacting with the user. The results from 615 test samples show that the NAO robot is able to correctly identify the Lego blocks configuration assembled by the user with an accuracy 81% of the time. Finally, we discuss the limitations of the proposed solution and we suggest future contributions that can overpass these limitations and boost the accuracy of our proposed solution.