LVR-Draw: A Language-Vision Pipeline for Robotic Drawing with Interactive Scene Verification and Correction

Abstract

This paper presents LVR-Draw, a fully local language–vision pipeline for robotic drawing that integrates structured scene generation, multimodal verification and correction, and deterministic execution within a unified Human–AI–Robot loop. Given a natural language prompt, a Large Language Model (LLM) generates a structured scene representation in a predefined format, which is rendered into an interpretable image. A Vision–Language Model (VLM) then performs visual inspection to detect inconsistencies in object placement and spatial relationships. These observations are processed by the LLM to produce structured editing operations, enabling iterative refinement of the scene. After validation, the refined scene is converted into executable robot instructions through a deterministic pipeline, supporting predictable and reproducible execution without using generative models for control. Experimental results suggest that the system can generate valid scene representations, support multimodal correction, and preserve drawing order during physical execution.