RoboGround：基于视觉-语言先验落地的机器人操作
RoboGround: Robotic Manipulation with Grounded Vision-Language Priors
AI总结：
本文提出RoboGround系统，利用落地掩码作为中间表示结合大规模视觉-语言模型先验，指导机器人操作策略网络，并构建自动化数据生成流程，显著提升了策略的泛化能力。
摘要：
机器人操作领域的最新进展凸显了中间表示在改善策略泛化方面的潜力。在这项工作中，我们探索将落地掩码作为有效的中间表示，它平衡了两大优势：（1）有效的空间引导，在指定目标物体和放置区域的同时，也传达了物体的形状和尺寸信息；（2）由在海量落地数据集上预训练的大规模视觉-语言模型驱动的广泛泛化潜力。我们引入了RoboGround，一个具备落地感知的机器人操作系统，它利用落地掩码作为中间表示来指导策略网络完成物体操作任务。为了进一步探索并提升泛化能力，我们提出了一种自动化流程，用于生成包含多种物体和指令的大规模模拟数据。大量实验证明了我们数据集的价值以及落地掩码作为中间引导的有效性，显著增强了机器人策略的泛化能力。
英文摘要：
Recent advancements in robotic manipulation have highlighted the potential of intermediate representations for improving policy generalization. In this work, we explore grounding masks as an effective intermediate representation, balancing two key advantages: (1) effective spatial guidance that specifies target objects and placement areas while also conveying information about object shape and size, and (2) broad generalization potential driven by large-scale vision-language models pretrained on diverse grounding datasets. We introduce RoboGround, a grounding-aware robotic manipulation system that leverages grounding masks as an intermediate representation to guide policy networks in object manipulation tasks. To further explore and enhance generalization, we propose an automated pipeline for generating large-scale, simulated data with a diverse set of objects and instructions. Extensive experiments show the value of our dataset and the effectiveness of grounding masks as intermediate guidance, significantly enhancing the generalization abilities of robot policies.
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。
