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<p>To address the problems of knowledge circular dependency and learning path ambiguity in quadruped robot programming learning, a dynamic teaching path generation method that integrates structural and semantic information is proposed. A domain knowledge graph covering core knowledge and prerequisite relationships is constructed. R-GCN (Relational Graph Convolutional Network) is used to generate structured node embeddings through link prediction tasks. SBERT (Sentence-Bidirectional Encoder Representations from Transformers) is also used to generate semantic vector representations of knowledge points and user tasks. For a given programming task, seed nodes are retrieved based on semantic similarity, and a priori knowledge subgraph is constructed by back-tracing. The number of seed nodes is fixed to the top 5 knowledge points ranked by semantic similarity. The similarity threshold is implicitly determined by this ranking strategy rather than a fixed value. The weighting coefficients used in edge scoring and node prioritization are set to α = 0.6 and β = 0.7 based on validation experiments described in Sect. 2.4 and 2.5. All models are implemented using PyTorch and trained on a single NVIDIA RTX 3090 GPU. The batch size is set to 512, and early stopping is applied based on validation loss with a patience of 10 epochs. Relationship strength is quantified by calculating the dot product of the embedding vectors, automatically identifying and pruning the weakest links in circular dependencies to achieve intelligent link breaking. During topological sorting, nodes with high semantic match to the task are prioritized for learning path generation. Experiments show that the proposed method achieves superior performance in the quadruped robot programming task, with a Top-3 score of 0.88, higher than CompGCN’s 0.73, a mean reciprocal rank of 0.68, and path integrity and structural rationality reaching 0.85 and 0.88, respectively. This method performs well in knowledge association modeling and path recommendation, effectively supports structured, task-driven programming teaching, and provides cognitively coherent learning support for beginners.</p>

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Dynamic teaching path generation for quadruped robot programming by integrating R-GCN and SBERT

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  • 发表日期：

    2026年5月25日

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  • Hualiang Wu

摘要

To address the problems of knowledge circular dependency and learning path ambiguity in quadruped robot programming learning, a dynamic teaching path generation method that integrates structural and semantic information is proposed. A domain knowledge graph covering core knowledge and prerequisite relationships is constructed. R-GCN (Relational Graph Convolutional Network) is used to generate structured node embeddings through link prediction tasks. SBERT (Sentence-Bidirectional Encoder Representations from Transformers) is also used to generate semantic vector representations of knowledge points and user tasks. For a given programming task, seed nodes are retrieved based on semantic similarity, and a priori knowledge subgraph is constructed by back-tracing. The number of seed nodes is fixed to the top 5 knowledge points ranked by semantic similarity. The similarity threshold is implicitly determined by this ranking strategy rather than a fixed value. The weighting coefficients used in edge scoring and node prioritization are set to α = 0.6 and β = 0.7 based on validation experiments described in Sect. 2.4 and 2.5. All models are implemented using PyTorch and trained on a single NVIDIA RTX 3090 GPU. The batch size is set to 512, and early stopping is applied based on validation loss with a patience of 10 epochs. Relationship strength is quantified by calculating the dot product of the embedding vectors, automatically identifying and pruning the weakest links in circular dependencies to achieve intelligent link breaking. During topological sorting, nodes with high semantic match to the task are prioritized for learning path generation. Experiments show that the proposed method achieves superior performance in the quadruped robot programming task, with a Top-3 score of 0.88, higher than CompGCN’s 0.73, a mean reciprocal rank of 0.68, and path integrity and structural rationality reaching 0.85 and 0.88, respectively. This method performs well in knowledge association modeling and path recommendation, effectively supports structured, task-driven programming teaching, and provides cognitively coherent learning support for beginners.