Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification
This addresses the challenge of reliable robotic planning in specialized environments for AI and robotics researchers, representing an incremental improvement through deeper integration of existing techniques.
The paper tackles the problem of LLMs struggling with long-horizon and complex robotic planning tasks by proposing a neuro-symbolic approach that integrates hierarchical planning, Knowledge Graph-based RAG, and symbolic verification, resulting in consistent significant advantages over baseline methods across varying task complexities and LLMs.
Large Language Models (LLMs) have shown promise as robotic planners but often struggle with long-horizon and complex tasks, especially in specialized environments requiring external knowledge. While hierarchical planning and Retrieval-Augmented Generation (RAG) address some of these challenges, they remain insufficient on their own and a deeper integration is required for achieving more reliable systems. To this end, we propose a neuro-symbolic approach that enhances LLMs-based planners with Knowledge Graph-based RAG for hierarchical plan generation. This method decomposes complex tasks into manageable subtasks, further expanded into executable atomic action sequences. To ensure formal correctness and proper decomposition, we integrate a Symbolic Validator, which also functions as a failure detector by aligning expected and observed world states. Our evaluation against baseline methods demonstrates the consistent significant advantages of integrating hierarchical planning, symbolic verification, and RAG across tasks of varying complexity and different LLMs. Additionally, our experimental setup and novel metrics not only validate our approach for complex planning but also serve as a tool for assessing LLMs' reasoning and compositional capabilities.