  We present GoalGrasp, a simple yet effective 6-DOF robot grasp pose detection
method that does not rely on grasp pose annotations and grasp training. Our
approach enables user-specified object grasping in partially occluded scenes.
By combining 3D bounding boxes and simple human grasp priors, our method
introduces a novel paradigm for robot grasp pose detection. First, we employ a
3D object detector named RCV, which requires no 3D annotations, to achieve
rapid 3D detection in new scenes. Leveraging the 3D bounding box and human
grasp priors, our method achieves dense grasp pose detection. The experimental
evaluation involves 18 common objects categorized into 7 classes based on
shape. Without grasp training, our method generates dense grasp poses for 1000
scenes. We compare our method's grasp poses to existing approaches using a
novel stability metric, demonstrating significantly higher grasp pose
stability. In user-specified robot grasping experiments, our approach achieves
a 94% grasp success rate. Moreover, in user-specified grasping experiments
under partial occlusion, the success rate reaches 92%.
