The field of human-machine interaction is rapidly evolving, with a growing focus on understanding the complex relationships between humans and machines. Recent studies have highlighted the importance of considering individual differences, such as personality traits and emotional responses, in the design of effective human-machine collaboration systems. For instance, research has shown that personality traits can influence performance in human-drone interaction, with extraverts and introverts exhibiting different strengths and weaknesses. Similarly, emotions have been found to play a crucial role in shaping trust in automated vehicles, with positive emotional responses facilitating trust calibration.

Noteworthy papers in this area include one that proposed a VR-based training system for human-drone interaction, which demonstrated the potential for optimizing human-drone collaboration by considering individual differences in personality traits and stress management. Another paper presented a framework for modeling human trust in a robot partner using physiological measures, gaze, and facial expressions, which could enable more efficient and safe human-robot interactions.