VeriGraph：用于可验证机器人规划的场景图
VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
AI总结：
VeriGraph通过整合视觉语言模型和场景图，提升机器人任务规划的可行性验证与修正，显著提高任务完成率。
摘要：
VeriGraph通过整合视觉语言模型和场景图，提升机器人任务规划的可行性验证与修正，显著提高任务完成率。
英文摘要：
Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrect action sequences. To address these limitations, we propose VeriGraph, a novel framework that integrates VLMs for robotic planning while verifying action feasibility. VeriGraph uses scene graphs as an intermediate representation to capture key objects and spatial relationships, enabling more reliable plan verification and refinement. The system generates a scene graph from input images and uses it to iteratively check and correct action sequences generated by an LLM-based task planner, ensuring constraints are respected and actions are executable. Our approach significantly enhances task completion rates across diverse manipulation scenarios, outperforming baseline methods by 58% on language-based tasks, 56% on tangram puzzle tasks, and 30% on image-based tasks. Qualitative results and code can be found at https://verigraph-agent.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
