用扩散模型估算重心，让机器人稳稳抓起不规则物体
Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation
- 基于基础模型的视觉框架，通过扩散模型定位未知物体重心
- 抓取成功率比传统关键点方法高49%，优于顶尖姿势驱动方法11%
- 对未见物体重心定位准确率达76%，适合复杂场景抓取任务
本研究提出一种针对质量分布不均物体的抓取方法，利用扩散模型实现对未知物体重心（CoG）的定位。在机器人抓取中，重心偏移常导致姿态不稳定，现有基于关键点或功能性的方法存在局限。研究构建了包含790张图像的数据集，标注了质量分布不均物体的重心关键点。设计了一种基于基础模型的视觉驱动框架，实现重心感知抓取。在真实场景中的实验表明，该方法相较传统关键点方法抓取成功率提升49%，优于当前最优的功能驱动方法11%。系统在未见过物体上仍保持76%的重心定位准确率，为精确稳定的抓取任务提供了新方案。
原文摘要 · Abstract (English)
This study presents a grasping method for objects with uneven mass distribution by leveraging diffusion models to localize the center of gravity (CoG) on unknown objects. In robotic grasping, CoG deviation often leads to postural instability, where existing keypoint-based or affordance-driven methods exhibit limitations. We constructed a dataset of 790 images featuring unevenly distributed objects with keypoint annotations for CoG localization. A vision-driven framework based on foundation models was developed to achieve CoG-aware grasping. Experimental evaluations across real-world scenarios demonstrate that our method achieves a 49\% higher success rate compared to conventional keypoint-based approaches and an 11\% improvement over state-of-the-art affordance-driven methods. The system exhibits strong generalization with a 76\% CoG localization accuracy on unseen objects, providing a novel solution for precise and stable grasping tasks.
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