# How Do Humans Recognize the Motion Arousal of Non-Humanoid Robots?
## 2 Related Work
### Existing Problems in the Expression of Emotional Movements of Robots  
The mainstream methods for robots to express emotions can be divided into two categories: one is to use machine learning algorithms, and the other is to find corresponding expression elements based on the robot's own characteristics according to emotional dimensions.  
The principle of using machine learning algorithms is to learn relevant features from a large corpus of labeled sample data, allowing the machine to automatically extract and map these features to generate expression trajectories. Common machine learning methods include spatiotemporal models such as Factored Conditional Restricted BoltzmannMachines (FCRBMs) [32], neural networks [33], and Factored Gaussian Process Dynamical Models (GPDM) [34]. Additionally, the use of large language models focuses on helping machines understand human-robot interaction scenarios [35], infer user preferences [36], and analyze responses to the environment and users [37]. Nevertheless, the generation of expressive behaviors still relies on datasets labeled with expressive behaviors. Therefore, the content learned by machine learning is generally relatively simple, and most works still focus on single tasks (such as walking [38]) and a specific structure. The quality of emotional actions for generating complex behaviors is not high, and there is a gap between human expectations [35]. Due to the need for biomimetic objects as a database, this method has limited applications on NHRs that have a significant appearance difference from humans and animals.  
Research on elements based on emotional dimensions often starts with selecting features from the robot's own characteristics. Factors related to arousal include speed [39] and acceleration [40], with higher speeds and accelerations representing higher levels of arousal. However, these elements are related to the characteristics of the machine itself and may not be applicable to different types of robots: a large machine cannot move as quickly as a small robot, and a robot that typically moves slowly cannot reduce arousal by moving even slower. Repetition [41] and emphasis [42] can increase arousal to some extent. However, the content of repetition and emphasis varies due to differences among robots. Without basic motion elements, these two cannot play a role.  
Additionally, in research on expressing arousal through movement, there are many cases that focus on robotic arms. For example, [PERSON] et al. [39] found that the amplitude of hand movements in the robot Nao is somewhat related to arousal. [PERSON] et al. [43] demonstrated arousal and emotional states by varying the amplitude of the Pepper robot's waving motion. [PERSON] and [PERSON] [44] used the concept of geometric entropy to calculate parameters of the robotic arm's spatial movement range and mapped them to the PAD emotional space.  
Furthermore, on highly simulated robotic arms, finger straightness [39] can also express arousal by showing the fingers: high arousal is displayed through more straight fingers, while low arousal is expressed through bent fingers.  
Overall, these elements are often fragmented, and their selection depends on the specific characteristics and details of the individual robot. Since they lack standardization and support, and do not explore the rules and mechanisms of expression from the perspective of human cognition, these elements cannot be transferred and used on other types of Non-Humanoid Robots (NHR). On one hand, it is difficult to ensure that the use of these elements can be correctly understood by ordinary people without experience in robot research and effectively promote human-robot communication. On the other hand, the methods used are not replicable and iterative, which is disadvantageous for knowledge accumulation and progress. They fail to provide a common research foundation for multimodal and interdisciplinary studies.