用场景图验证机器人任务规划，提升执行成功率
VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
- 用场景图作为中间表示，捕捉物体与空间关系
- 在语言、拼图、图像任务上分别提升58%、56%、30%完成率
- 适合需要可靠动作验证的机器人自主规划场景
视觉语言模型（VLM）的发展为机器人任务规划带来了新可能，但常生成错误的动作序列。为此，我们提出VeriGraph框架，将VLM用于机器人规划的同时进行动作可行性验证。VeriGraph利用场景图作为中间表示，捕捉关键物体及其空间关系，实现更可靠的计划验证与修正。系统从输入图像生成场景图，通过迭代检查并修正基于大语言模型的任务规划生成的动作序列，确保约束满足且动作可执行。该方法在多种操作场景下显著提升任务完成率，在语言基任务上比基线高58%，拼图任务高56%，图像基任务高30%。定性结果与代码见https://verigraph-agent.github.io。
原文摘要 · Abstract (English)
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.
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