ROG-Grasp estimates produce orientation from root surface geometry via YOLO detection and point cloud plane fitting to generate stable grasp poses and constrained motion plans, achieving higher reliability and speed than VLA policies in tomato and onion experiments.
A novel ap- proach to tomato harvesting using a hybrid gripper with semantic seg- mentation and keypoint detection,
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ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement
ROG-Grasp estimates produce orientation from root surface geometry via YOLO detection and point cloud plane fitting to generate stable grasp poses and constrained motion plans, achieving higher reliability and speed than VLA policies in tomato and onion experiments.