AOMGen: 为关节物体操控生成逼真、物理一致的演示
AOMGen: Photoreal, Physics-Consistent Demonstration Generation for Articulated Object Manipulation
AI总结：
本文提出AOMGen框架，通过单次真实扫描、演示和数字资产库生成逼真训练数据，提升机器人操控任务的成功率。
摘要：
近期视觉-语言-动作（VLA）和世界模型方法的进步提高了机器人操控和物体交互任务的泛化能力。然而，成功执行此类任务依赖于大量、成本高的真实演示，尤其是对关节物体的精细操控。为此，我们提出了AOMGen，一种可扩展的数据生成框架，通过单次真实扫描、演示和数字资产库生成逼真训练数据。该框架合成同步多视角RGB图像，与动作命令和状态注释时间对齐，并系统地变化相机视角、物体样式和物体姿态，将单次执行扩展为多样化的数据集。实验结果表明，对AOMGen数据进行微调可将VLA策略的成功率从0%提升至88.7%，且策略在未见过的物体和布局上进行测试。
英文摘要：
Recent advances in Vision-Language-Action (VLA) and world-model methods have improved generalization in tasks such as robotic manipulation and object interaction. However, Successful execution of such tasks depends on large, costly collections of real demonstrations, especially for fine-grained manipulation of articulated objects. To address this, we present AOMGen, a scalable data generation framework for articulated manipulation which is instantiated from a single real scan, demonstration and a library of readily available digital assets, yielding photoreal training data with verified physical states. The framework synthesizes synchronized multi-view RGB temporally aligned with action commands and state annotations for joints and contacts, and systematically varies camera viewpoints, object styles, and object poses to expand a single execution into a diverse corpus. Experimental results demonstrate that fine-tuning VLA policies on AOMGen data increases the success rate from 0% to 88.7%, and the policies are tested on unseen objects and layouts.
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。
