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Robots' acceptability among humans and their sociability can be significantlyenhanced by incorporating human-like reactions. Humans can react toenvironmental events very quickly and without thinking. An instance wherehumans show natural reactions is when they encounter a sudden and loud soundthat startles or frightens them. During such moments, individuals mayinstinctively move their hands, turn toward the origin of the sound, and try todetermine the event's cause. This inherent behavior motivated us to explorethis less-studied part of social robotics. In this work, a multi-modal systemcomposed of an action generator, sound classifier, and YOLO object detector wasdesigned to sense the environment and, in the presence of sudden loud sounds,show natural human fear reactions; and finally, locate the fear-causing soundsource in the environment. These valid generated motions and inferences couldimitate intrinsic human reactions and enhance the sociability of robots. Formotion generation, a model based on LSTM and MDN networks was proposed tosynthesize various motions. Also, in the case of sound detection, a transferlearning model was preferred that used the spectrogram of the sound signals asits input. After developing individual models for sound detection, motiongeneration, and image recognition, they were integrated into a comprehensive"fear" module implemented on the NAO robot. Finally, the fear module was testedin practical application and two groups of experts and non-experts (in therobotics area) filled out a questionnaire to evaluate the performance of therobot. We indicated that the proposed module could convince the participantsthat the Nao robot acts and reasons like a human when a sudden and loud soundis in the robot's peripheral environment, and additionally showed thatnon-experts have higher expectations about social robots and their performance.

Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO  through Sound-Based Reactions to Fearful and Shocking Events for Enhanced  Sociability