Learning Agent Skills from Demonstrations and Generating Knowledge Graphs

Today, skill transfer from humans to agents via demonstrations is a widely adopted approach. However, prior research primarily focused on recording data generated during demonstrations, leading to challenges such as poor interpretability of the skill transfer process, limited transferability and generalization capabilities of demonstrations, and insufficient visualization. To address these issues, this paper proposes a method for agent skill learning and knowledge graph generation based on demonstrations. The contributions include the following. First, to enhance the interpretability of the skill transfer process, we construct demonstration skills. Second, to improve the transferability and generalization of demonstrations, we propose a DeepSeek-based method for decomposing demonstration skills and generating behavior trees. Finally, to strengthen visualization, we introduce a hierarchical skill generation method for agents using knowledge graphs. Experiments conducted in 3C assembly and satellite assembly scenarios demonstrate that our method substantially enhances the interpretability of the demonstration process.