Human-centric robotic manipulation in construction: generative adversarial networks based physiological computing mechanism to enable robots to perceive workers' cognitive load

With the recent advancements in sensing technologies, mechatronics, and artificial intelligence, collaborative robots are deployed on construction sites to assist workers in performing physically demanding tasks. However, the human-robot collaboration (HRC) can bring several occupational challenges to workers, ranging from physical collisions to adverse psychological impacts. To date, most of the literature on HRC has focused on addressing physical safety challenges, while very few have considered the psychological safety of the workers. To bridge this gap, by integrating generative adversarial network, autoencoder, machine learning, and robot adaptation techniques, this study proposes a novel physiological computing system that enables the collaborative robot to efficiently perceive workers' psychological states and regulate its performance seamlessly. The results showed that the proposed system allowed the robot to adjust its performance as per workers' cognitive load level with 89.6% accuracy. The findings revealed the potential of the proposed system in facilitating safe HRC in construction.