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

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.