Robots' acceptability among humans and their sociability can be significantly
enhanced by incorporating human-like reactions. Humans can react to
environmental events very quickly and without thinking. An instance where
humans display natural reactions is when they encounter a sudden and loud sound
that startles or frightens them. During such moments, individuals may
instinctively move their hands, turn toward the origin of the sound, and try to
determine the event's cause. This inherent behavior motivated us to explore
this less-studied part of social robotics. In this work, a multi-modal system
composed of an action generator, sound classifier, and YOLO object detector was
designed to sense the environment and, in the presence of sudden loud sounds,
show natural human fear reactions, and finally, locate the fear-causing sound
source in the environment. These unique and valid generated motions and
inferences could imitate intrinsic human reactions and enhance the sociability
of robots. For motion generation, a model based on LSTM and MDN networks was
proposed to synthesize various motions. Also, in the case of sound detection, a
transfer learning model was preferred that used the spectrogram of sound
signals as its input. After developing individual models for sound detection,
motion generation, and image recognition, they were integrated into a
comprehensive fear module that was implemented on the NAO robot. Finally, the
fear module was tested in practical application and two groups of experts and
non-experts filled out a questionnaire to evaluate the performance of the
robot. Given our promising results, this preliminary exploratory research
provides a fresh perspective on social robotics and could be a starting point
for modeling intrinsic human behaviors and emotions in robots.

通过将人类的反应融入机器人中，可以显著提高机器人在人类中的可接受度和社交性。本研究探索了社交机器人中较少研究的部分，设计了一个多模态系统，通过感知环境并在突然出现大声音时展示人类自然恐惧反应，最终定位引起恐惧的声音来源。通过整合运动生成、声音检测和图像识别等模块，该研究在 NAO 机器人上实现了恐惧模块，并通过问卷调查对其性能进行了评估。该初步探索性研究提供了一个新的社交机器人视角，并可作为在机器人中建模人类内在行为和情感的起点。

人类反应模拟：利用基于声音的反应增强 NAO 的社交性

Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO  through Sound-Based Reactions for Enhanced Sociability

We define and analyze a multi-agent multi-armed bandit problem in which
decision-making agents can observe the choices and rewards of their neighbors.
Neighbors are defined by a network graph with heterogeneous and stochastic
interconnections. These interactions are determined by the sociability of each
agent, which corresponds to the probability that the agent observes its
neighbors. We design an algorithm for each agent to maximize its own expected
cumulative reward and prove performance bounds that depend on the sociability
of the agents and the network structure. We use the bounds to predict the rank
ordering of agents according to their performance and verify the accuracy
analytically and computationally.

研究和分析了一个多智能体多臂赌博问题，其中智能体可观察到邻居的选择和奖励，这些邻居由具有异构和随机互连的网络图定义，这些互动由每个智能体的社交性决定，我们针对每个智能体设计算法来最大化其自己的预期累积奖励，并证明依赖于智能体和网络结构的社交性能边界。使用这些边界来预测智能体的性能排名，并进行了理论和计算上的验证。