Preprint
ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement
ArXiv.org
30 May 2026
Abstract
Orientation-aware manipulation is essential in post-harvest agricultural processing, where produce must be grasped and placed in consistent configurations. This paper presents ROG-Grasp, a geometry-based robotic grasping and placement framework that estimates the produce orientation from root surface geometry using RGB-D perception. A YOLO-based root detector and point cloud plane fitting are used to infer the root normal, enabling stable grasp pose generation and orientation-constrained Cartesian motion planning. Experiments on tomatoes and onions demonstrate high success rates and stable execution time in both isolated and cluttered scenarios. Compared with vision-language-action (VLA) policies, the proposed method achieves more reliable and accurate grasp completion with faster execution. These results highlight the effectiveness of geometry-driven perception for practical orientation-controlled manipulation tasks. A video of our paper is available online https://youtu.be/Ir2UtGODdMo.
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Details
- Title
- ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement
- Creators
- Zijian AnAugustus SrokaRan YangBill CaiSatoru EtoBrian PoonKelvin CaiShijie GengFeng LiuYiming FengLifeng Zhou
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science
- Other Identifier
- 991022188872904721