V-CAGE：用于机器人操作的视觉闭环代理生成引擎
V-CAGE: Vision-Closed-Loop Agentic Generation Engine for Robotic Manipulation
AI总结：
V-CAGE通过闭环代理生成框架实现机器人数据合成，结合语义布局规划和视觉自验证，提升环境生成的语义结构和可达性，解决传统方法在场景生成中的不足。
摘要：
扩大视觉-语言-动作（VLA）模型需要大量语义一致且物理可行的数据集。然而，现有场景生成方法缺乏上下文感知，难以合成富含语义信息的高保真环境，常导致目标位置不可达，任务提前失败。我们提出V-CAGE（视觉闭环代理生成引擎），一种自主机器人数据合成框架。不同于传统脚本流程，V-CAGE作为具身代理系统，利用基础模型连接高层语义推理与低层物理交互。具体而言，我们引入图像修复引导的场景构建，系统性安排上下文感知布局，确保生成场景语义结构和运动学可达。为确保轨迹正确性，我们整合功能元数据与基于视觉-语言模型的闭环验证机制，作为视觉批评者严格过滤静默失败并切断误差传播链。最后，为克服大规模视频数据集的存储瓶颈，我们实现基于感知的压缩算法，实现超过90%的文件大小缩减而不影响下游VLA训练效果。通过集中语义布局规划和视觉自验证，V-CAGE自动化端到端流程，实现高可扩展性合成多样化高质量的机器人操作数据集。
英文摘要：
Scaling Vision-Language-Action (VLA) models requires massive datasets that are both semantically coherent and physically feasible. However, existing scene generation methods often lack context-awareness, making it difficult to synthesize high-fidelity environments embedded with rich semantic information, frequently resulting in unreachable target positions that cause tasks to fail prematurely. We present V-CAGE (Vision-Closed-loop Agentic Generation Engine), an agentic framework for autonomous robotic data synthesis. Unlike traditional scripted pipelines, V-CAGE operates as an embodied agentic system, leveraging foundation models to bridge high-level semantic reasoning with low-level physical interaction. Specifically, we introduce Inpainting-Guided Scene Construction to systematically arrange context-aware layouts, ensuring that the generated scenes are both semantically structured and kinematically reachable. To ensure trajectory correctness, we integrate functional metadata with a Vision-Language Model based closed-loop verification mechanism, acting as a visual critic to rigorously filter out silent failures and sever the error propagation chain. Finally, to overcome the storage bottleneck of massive video datasets, we implement a perceptually-driven compression algorithm that achieves over 90\% filesize reduction without compromising downstream VLA training efficacy. By centralizing semantic layout planning and visual self-verification, V-CAGE automates the end-to-end pipeline, enabling the highly scalable synthesis of diverse, high-quality robotic manipulation datasets.
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。
