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Dynamic teaching path generation for quadruped robot programming by integrating R-GCN and SBERT.
1Yantai Institute of Science and Technology, Yantai, 265600, China. wuhl@jxut.edu.cn.
Scientific Reports
|May 25, 2026
Summary
This study introduces a dynamic method for generating quadruped robot programming learning paths, resolving knowledge gaps and ambiguity. The approach uses graph neural networks and semantic similarity to create effective, structured educational sequences for beginners.
Area of Science:
- Robotics
- Artificial Intelligence
- Education Technology
Background:
- Quadruped robot programming learning faces challenges with circular knowledge dependencies and unclear learning paths.
- Existing methods struggle to provide structured, task-driven educational support for complex programming tasks.
Purpose of the Study:
- To propose a dynamic teaching path generation method integrating structural and semantic information for quadruped robot programming.
- To address knowledge circular dependency and learning path ambiguity in educational contexts.
Main Methods:
- Constructed a domain knowledge graph with core knowledge and prerequisite relationships.
- Employed Relational Graph Convolutional Network (R-GCN) for structured node embeddings and Sentence-Bidirectional Encoder Representations from Transformers (SBERT) for semantic vectors.
- Implemented a back-tracing approach from semantically similar seed nodes to construct a priori knowledge subgraphs.
Main Results:
- Achieved a Top-3 score of 0.88 in quadruped robot programming tasks, outperforming CompGCN (0.73).
- Demonstrated superior mean reciprocal rank (0.68) and high path integrity (0.85) and structural rationality (0.88).
- Effectively pruned circular dependencies and prioritized semantically relevant knowledge points.
Conclusions:
- The proposed method significantly enhances knowledge association modeling and learning path recommendation for programming education.
- Provides cognitively coherent and structured, task-driven learning support, particularly beneficial for beginners in robotics programming.
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