目的 服务机器人逐渐应用于情感陪伴和情感疗愈，探寻萌形态机器人的情感疗愈机制，研究影响用户情感体验的机器人萌形态特征要素。方法 解析萌形态语义并选取萌形态机器人样本，开展人机交互实验，测量用户在压力、静息，以及人机交互过程中的面部肌电信号(fEMG)、皮肤电信号(EDA)和心率变异性信号(HRV)。交互后邀请被试填写机器人萌体验主观量表。结论 fEMG指标表明在压力刺激后与萌形态机器人交互能有效唤醒积极情绪，且机器人萌评分越高用户的积极情绪评分越高，EDA指标表明机器人萌评分越高被试情绪唤醒度也越高;同形态下机器人萌表情使被试HRV信号中的SDNN指标显著提高，同形态下柔软材质的触觉交互使被试HRV信号中的SDNN、VLF指标显著提高，相同动作交互下萌形态评分更高的机器人使被试EMG信号、EDA信号、HRV信号指标显著提高。最后，选取与萌体验具有相关性的生理指标，构建机器人萌体验与用户生理指标的关系模型，相较于BP神经网络，WOA优化BP神经网络建立的关系模型表现出更低的MSE值和更高的R²。

Service robots are gradually applied to emotional accompaniment and emotional healing. The work aims to explore the emotional healing mechanism of cuteness morphology robots, and to study the elements of cuteness morphology features of robots that affect users' emotional experience. The semantics of cuteness morphology were analyzed and a sample of cuteness morphology robots was selected to conduct human-robot interaction experiments. Users' facial electromyographic signals (fEMG), electrodermal signals (EDA), and heart rate variability signals (HRV) during stress, calmness, and human-robot interaction were measured, and subjects were invited to fill out the subjective questionnaire of the robot's cute experience after the interaction. In conclusion, fEMG indicators show that interacting with the cuteness morphology robot after a stressful stimulus can effectively arouse positive emotions. The higher the robot cuteness score, the higher the user's positive emotion score. The EDA indicators show that the subject's emotional arousal degree is also higher. The same robot form with cute expression makes the SDNN indicators in the HRV signal of the subject significantly higher. The same robot form with the tactile interaction of the soft material makes the SDNN and VLF indicators in the HRV signal significantly higher; The same action interaction with the robot of higher cuteness makes the subjects’ EMG signal, EDA signal, and HRV signal indicators significantly higher. The SDNN and VLF indexes in HRV signals are significantly increased by the same robot form. The EMG signal, EDA signal, and HRV signal indexes are significantly increased by the same robot form in the same action interaction. Finally, physiological indicators that have correlation with cute experience are selected to construct the relationship model between robot cute experience and user physiological indicators. Compared with the BP neural network, the relationship model established by the WOA optimized BP neural network shows lower MSE and higher R2.