# How Do Humans Recognize the Motion Arousal of Non-Humanoid Robots?
## 6 General Discussion  
Previous studies on robot action expression, which use machine learning for motion replication, often result in actions that do not fully align with human expectations [35]. These studies focus on using the robot's own characteristic elements for expression without exploring the rules and mechanisms of expression from the perspective of human cognition. On the one hand, it is difficult to ensure that ordinary people without experience in robotics research can correctly understand the study. On the other hand, the methods used are not replicable and iterative, which is detrimental to the accumulation of knowledge and progress. Based on the Gestalt theory in psychology, this study proposes a general hypothesis: human recognition of robot emotional actions also stems from the perception of overall motion, independent of the mechanical appearance. It further inquires whether there arepotential mechanisms, such as expressible elements that transcend appearance, through which humans can perceive the arousal level expressed by the movements of NHRs. The experimental results demonstrate that amplitude can help manifest arousal not only in NHRs dominated by IK constraints but also in those dominated by FK constraints and in NHRs where both IK and FK constraints play a dominant role. This evidence suggests that Gestalt psychology has certain applicability in the field of robot emotional action expression. This study introduces Gestalt psychology to analyze human cognitive patterns and applies it to the emotional action expression of NHRs, pioneering a new research pathway. This approach aids NHRs in learning the rules of emotional expression from limited examples, not only enhancing the efficiency of machine learning but also broadening the applicability to a wider range of NHR types. It liberates the subjects of emotional expression from being confined to anthropomorphic or biomimetic robots. The action expression algorithm developed based on human cognitive patterns can enrich and streamline the emotional expression of robots. This innovative application has the potential to bridge the gap between robot emotional expression and human expectations, thus propelling the further development of robot emotional interaction technology. Additionally, it can foster knowledge accumulation and scientific progress, providing a unified foundation for multimodal and interdisciplinary research. In contrast to visual cues such as facial expressions, voice modulation, and LED color changes, the utilization of motion amplitude for expression possesses distinctive attributes. Facial expressions, while intuitive and readily discernible, are highly effective in conveying complex emotions and social cues but are constrained by the design limitations of NHRs. Motion amplitude, however, is not bound by the robot's physical appearance and is inherently more noticeable. Voice modulation, with its ability to communicate a wealth of emotional content through varying pitches, volumes, and rhythms, can be mitigated by environmental noise and may be inappropriate in sound-sensitive settings. Motion amplitude, conversely, offers a more spatial and intuitive form of expression, less susceptible to acoustic interference. Visual cues like LED color changes can effectively signal emotion and rapidly engage user attention in certain contexts, yet they are limited in expressing intricate emotional states. Motion amplitude, however, can transcend this limitation, showcasing the rich layers of emotions in a more nuanced and dynamic way. Consequently, in the design and execution of emotional expression for NHRs, motion amplitude emerges as a distinctive and effective method. The findings of this research are poised to make significant contributions in practical settings. For instance, in the domain of service robotics, the discovery that motion amplitude influences users' emotional perceptions can be leveraged to elevate service quality and user experience.