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arXiv · 2506.21057

Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions

Zhuochen Miao·Jun Lv·Hongjie Fang·Yang Jin·Cewu Lu
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Abstract

Imitation learning has emerged as a powerful paradigm in robot manipulation, yet its generalization capability remains constrained by object-specific dependencies in limited expert demonstrations. To address this challenge, we propose knowledge-driven imitation learning, a framework that leverages external structural semantic knowledge to abstract object representations within the same category. We introduce a novel semantic keypoint graph as a knowledge template and develop a coarse-to-fine template-matching algorithm that optimizes both structural consistency and semantic similarity. Evaluated on three real-world robotic manipulation tasks, our method achieves superior performance, surpassing image-based diffusion policies with only one-quarter of the expert demonstrations. Extensive experiments further demonstrate its robustness across novel objects, backgrounds, and lighting conditions. This work pioneers a knowledge-driven approach to data-efficient robotic learning in real-world settings. Code and more materials are available on https://knowledge-driven.github.io/.

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Zhuochen Miao, Jun Lv, Hongjie Fang, Yang Jin, Cewu Lu. 2025-06-26. Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions. https://arxiv.org/abs/2506.21057

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