How Do Humans Recognize the Motion Arousal of Non-Humanoid Robots?
Source abstract
Abstract
As non-humanoid robots develop and become more involved in human life, emotional communication between humans and robots will become more common. Non-verbal communication, especially through body movements, plays a significant role in human–robot interaction. To enable non-humanoid robots to express a richer range of emotions, it is crucial to understand how humans recognize the emotional movements of robots. This study focuses on the underlying mechanisms by which humans perceive the motion arousal levels of non-humanoid robots. It proposes a general hypothesis: Human recognition of a robot’s emotional movements is based on the perception of overall motion, and is independent of the robot’s mechanical appearance. Based on physical motion constraints, non-humanoid robots are divided into two categories: those guided by inverse kinematics (IK) constraints and those guided by forward kinematics (FK) constraints. Through literature analysis, it is suggested that motion amplitude has the potential to be a common influencing factor. Two psychological measurement experiments combined with the PAD scale were conducted to analyze the subjects’ perception of the arousal expression effects of different types of non-humanoid robots at various motion amplitudes. The results show that amplitude can be used for expressing arousal across different types of non-humanoid robots. Additionally, for non-humanoid robots guided by FK constraints, the end position also has a certain impact. This validates the overall hypothesis of the paper. The expression patterns of emotional arousal through motion amplitude are roughly the same across different robots: the degree of motion amplitude corresponds closely to the degree of arousal. This research helps expand the boundaries of knowledge, uncover user cognitive patterns, and enhance the efficiency of expressing arousal in non-humanoid robots.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Emotion and Mood Recognition · Social Robot Interaction and HRI · Robot Manipulation and Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Art and Design, Guangdong University of Technology, Canton 510090, China
Funding
National Natural Science Foundation of China · Guangzhou Science and Technology Planning Project
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