Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions

2025-06-26

AI Summary

Imitation learning for robotic manipulation suffers from limited generalization due to sparse expert demonstrations and strong dependence on specific objects or environmental conditions. To address this, we propose augmenting imitation learning with external structured semantic knowledge. Our method introduces a semantic keypoint graph as a transferable knowledge template, designs a coarse-to-fine template matching algorithm that jointly optimizes structural consistency and semantic similarity, and embeds the graph-structured prior into the imitation learning framework to enable knowledge-guided policy learning. Evaluated on three real-world robotic manipulation tasks, our approach achieves superior performance over image-based diffusion policies using only 25% of the expert demonstrations. It demonstrates significant improvements in robustness and data efficiency when deployed on novel objects, under varying backgrounds, and across diverse lighting conditions.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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/.

Problem

• Enhancing generalization in imitation learning across diverse conditions
• Reducing object-specific dependencies in limited expert demonstrations
• Improving robotic manipulation with knowledge-driven semantic abstraction

Innovation

• Leverages external structural semantic knowledge
• Introduces semantic keypoint graph template
• Develops coarse-to-fine template-matching algorithm