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%.

翻译：我们提出GoalGrasp——一种简洁高效的六自由度机器人抓取姿态检测方法，该方法无需依赖抓取姿态标注和抓取训练。所提方法能够实现在部分遮挡场景中按用户指定目标进行抓取。通过结合三维边界框与简单的人体抓取先验知识，该方法开创了机器人抓取姿态检测的新范式。首先，我们采用无需三维标注的三维目标检测器RCV实现新场景的快速三维检测。利用三维边界框与人体抓取先验知识，本方法可实现密集抓取姿态检测。实验评估涉及依据形状划分为7个类别的18种常见目标。在无抓取训练条件下，本方法为1000个场景生成密集抓取姿态。采用新提出的稳定性度量指标，将本方法生成的抓取姿态与现有方法进行对比，结果表明本方法具有显著更高的抓取姿态稳定性。在用户指定目标机器人抓取实验中，本方法达到94%的抓取成功率；而在部分遮挡场景下的指定目标抓取实验中，成功率仍可达92%。