CL-RAG: A Closed-Loop Multimodal Retrieval-Augmented Generation Architecture for Robust Human-Robot Control Interaction

Recent advancements in Large Language Mod-els (LLMs) such as Claude 3.5 and LLaMA 3, paired with Retrieval-Augmented Generation (RAG), offer promising opportunities for intelligent, context-aware robotic systems. This paper proposes a modular architecture that integrates enterprise-grade RAG technologies—originally developed for corporate knowledge management—into real-time control of bipedal and interactive robots. By adapting components such as ChromaDB, Docling, and AWS Bedrock, we demonstrate how unstructured sensor data, dynamic environment cues, and human language commands can be seamlessly processed to drive physically grounded robot behaviors. A proof-of-concept implementation shows significant improvements in instruction comprehension, semantic robustness, and safety, verified in industrial and inspection scenarios.