仅用彩色图像实现高精度抓取，降低仓库机器人硬件成本
OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots
- 基于基础模型，仅用RGB图像预测抓取位姿
- 真实场景抓取成功率达82.3%，无需深度传感器
- 训练数据为合成数据，可泛化到未见过的新物体
在仓储环境中，机器人需具备应对多种物体的可靠抓取能力。高效部署要求硬件少、对新产品泛化能力强、适应多变环境。现有方法通常依赖深度传感器获取结构信息，但存在成本高、配置复杂和技术限制等问题。受计算机视觉进展启发，我们提出一种新方法，仅使用RGB图像，借助基础模型增强吸盘式抓取。模型仅在合成数据集上训练，却能有效泛化至真实机器人及大量未见新物体。实际应用中，该网络实现82.3%的抓取成功率。项目代码与数据将发布于http://optigrasp.github.io。
原文摘要 · Abstract (English)
In warehouse environments, robots require robust picking capabilities to manage a wide variety of objects. Effective deployment demands minimal hardware, strong generalization to new products, and resilience in diverse settings. Current methods often rely on depth sensors for structural information, which suffer from high costs, complex setups, and technical limitations. Inspired by recent advancements in computer vision, we propose an innovative approach that leverages foundation models to enhance suction grasping using only RGB images. Trained solely on a synthetic dataset, our method generalizes its grasp prediction capabilities to real-world robots and a diverse range of novel objects not included in the training set. Our network achieves an 82.3\% success rate in real-world applications. The project website with code and data will be available at http://optigrasp.github.io.
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