A cognitive strategy for service robots in recognizing emotional attribute of objects

IF 0.8 Q4 ROBOTICS
Hao Wu, Jiaxuan Du, Qin Cheng, Qing Ma
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引用次数: 0

Abstract

With the advancement of service robots, discovering the emotional needs of users is becoming increasingly important. Unlike research focusing solely on human facial expression recognition or image sentiment recognition, our work proposes that the objects in the environment also impact human emotions, such as candy, which can make people happy. Therefore, studying the impact of objects on human emotions is crucial for service robots to regulate human emotions and provide more satisfactory services. In this work, we first propose the emotional attribute of objects: the ability to improve people’s moods. And we propose a strategy for recognizing this attribute. To achieve this, we first construct the H–S object emotional attribute image dataset, which contains different objects with pleasant or unpleasant emotion labels for people. We then propose the YOLOv3-SESA object detection model. By incorporating YOLOv3 with the SESA attention module, the model focuses more on the target objects, achieving higher recognition accuracy for small objects in the environment. We gain the correlation frequency between objects and emotion labels and convert it into emotional attribute probability values. Objects with the value exceeding a predefined threshold are defined as having an emotional attribute. Our experiments validate the effectiveness of our approach, yielding a list of common objects that can please users. By leveraging the knowledge of object emotional attributes, service robots can proactively provide emotionally appealing objects to humans, offering psychological comfort when they are depressed.

服务机器人识别物体情感属性的认知策略
随着服务机器人的发展,发现用户的情感需求变得越来越重要。与只关注人类面部表情识别或图像情感识别的研究不同,我们的工作提出,环境中的物体也会影响人类的情绪,比如糖果,它能让人开心。因此,研究物体对人类情绪的影响对于服务机器人调节人类情绪、提供更满意的服务至关重要。在这项工作中,我们首先提出了物体的情感属性:改善人们情绪的能力。我们还提出了识别这一属性的策略。为此,我们首先构建了 H-S 物体情感属性图像数据集,该数据集包含不同的物体,并为人们提供了令人愉快或不愉快的情感标签。然后,我们提出了 YOLOv3-SESA 物体检测模型。通过将 YOLOv3 与 SESA 注意力模块相结合,该模型更专注于目标对象,对环境中的小对象实现了更高的识别准确率。我们获取物体与情感标签之间的相关频率,并将其转换为情感属性概率值。概率值超过预定阈值的物体被定义为具有情感属性。我们的实验验证了我们方法的有效性,并得出了一份能取悦用户的常见物体列表。利用物体情感属性的知识,服务机器人可以主动为人类提供具有情感吸引力的物体,在人类情绪低落时提供心理安慰。
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来源期刊
CiteScore
2.00
自引率
22.20%
发文量
101
期刊介绍: Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics
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