## 6 Conclusions  
The proposal established a domain ontology model of the home environment in order to standardize the data information to enable home service robots to acquire information on the home environment. A decision-making template was developed using a knowledge graph to acquire missing information and perform task reasoning. Knowledge reasoning occurred at two levels (attribute and instance) to ensure the stable and effective use of the knowledge graph.  
The proposed transfer learning method exhibited effective feature extraction using different datasets. The use of lightweight neural networks optimized the model's inference speed. Therefore, the model exhibited faster inference speed and higher prediction accuracy than classical deep neural network and machine learning methods. By incorporating transfer learning strategies, our model retains efficiency while being fine-tuned for specific indoor environments, significantly improving classification accuracy and real-time response capabilities. This approach demonstrates strong advantages in addressing issues related to data scarcity in service robots and the need for rapid responses, especially in dynamic indoor environments where quick task execution is essential.  
This study also has some limitations. For instance, variations in lighting and occlusions are common in household environments, posing challenges to the robot's recognition tasks. It is necessary to further enhance robustness to these variations through data augmentation techniques and real-time adaptive strategies. Additionally, the boundaries of functional areas in homes are often ambiguous, making features prone to confusion. In future work, multi-task learning can be introduced to integrate the features of functional areas and objects, thereby improving recognition performance in complex scenarios. It should be mentioned that, in future work, we will also focus on metrics such as memory usage and scalability in various household environments to enhance the overall performance of the designed model.  
Conceptualization, Y.W.; methodology, R.Y.; data curation, P.W.; writing--original draft preparation, R.Y.; writing--review and editing, K.Z.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (62276028), the Major Research Plan of the National Natural Science Foundation of China (92267110), and the Young Backbone Teacher Support Plan of Beijing Information Science &Technology University (YBT202414).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Category** & **Accuracy** & **Category** & **Accuracy** & **Category** & **Accuracy** & **Category** & **Accuracy** \\ \hline \hline   Trowel & 0.969 & Tenderizer & 0.866 & Ladle & 0.899 & Spoon & 0.772 \\   Scoop & 0.993 & Shovel & 0.996 & Bowl & 0.961 & Knife & 0.928 \\   Shears & 0.938 & Saw & 1.000 & Pot & 0.753 & & \\   Mug & 0.981 & Hammer & 0.904 & Mallet & 0.699 & & \\   Turner & 0.987 & Cup & 0.965 & Scissors & 0.981 & & \\ \hline \end{tabular}
\end{table}
Table 3: Prediction accuracy for various tools.