Transformer-Based Relationship Inference Model for Household Object Organization by Integrating Graph Topology and Ontology

In domestic environments, the conventional or- ganization of objects by service robots often relies on the inherent properties of each object, such as placing fragile bowls in enclosed cupboards. However, this approach tends to overlook the importance of the orderly arrangement of objects, neglecting the specific placement order of bowls within the cabinet. In practice, effective object organization necessitates consideration of both individual properties and the relationships defined by these properties. In this paper, we have constructed a specialized dataset encompassing the ontological properties of household objects along with their relationships. Furthermore, we have introduced a graph-based model to explicitly represent these relationships and proposed a novel feature extraction technique that integrates the Graph Attention Network (GAT) with the BERT model to predict the relationships among objects. Subsequently, we utilized the Transformer framework to train a model, enabling it to infer relationships between objects. Experimental validation demonstrates the effectiveness of our approach in accurately predicting relationships between household objects, thus facilitating their orderly organization. Our approach significantly augments the object organization capabilities for service robots by accurately predicting the relationships among household objects. Our code is available at: https://github.com/Li-XD-Pro/Household-Object-Organization