LLM-RSPF: Large Language Model-Based Robotic System Planning Framework for Domain Specific Use-cases

The employment of large language models (LLMs) for task planning and reasoning has emerged as a focal point of interest within the robotics research community. However, directly applying LLMs, even with large token-sized prompts, does not achieve the task planning performance required for an industrial-grade domain-specific use-case (DSU). This work aims to overcome the obstacles of a robotic task planner for DSUs by introducing a novel planning framework, LLM-RSPF (Large Language Model-based Robotic System Planning Framework). Central to the LLM-RSPF is a novel robotic system ontology that organizes the components of the robotic system in a coherent and a systematic manner. The ontology empowers the LLM-RSP F to efficiently capture a contextual representation of the DSU using the LLMs. Subsequently, the research introduces a LLM-tuning regimen referred as chain of hierarchical thought (CoHT), specifically crafted to complement the proposed system ontology. Integrating these two components, the LLM-RSPF aims to enhance the accuracy, robustness, and throughput of a robotic system in a cost-effective manner. In addition, the research presents an empirical methodology to generate the LLM-tuning dataset size for a guaranteed performance. The LLM-RSPF is validated on a retail order-fulfillment use-case thereby, illustrating the efficacy of the framework. Through rigorous evaluation, the LLM-RSPF demonstrates exceptional performance on the generated dataset, effectively meeting the DSU objectives.