Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternatively, we explore the grasp generation from the RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects the projection of the gripper keypoints in the image space and then recover the SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive shape and the grasp family is constructed to examine our idea. Metric-based evaluation reveals that our method outperforms the baselines in terms of the grasp proposal accuracy, diversity, and the time cost. Finally, robot experiments show high success rate, demonstrating the potential of the idea in the real-world applications.

翻译：从点云输入中学习六自由度抓取已取得巨大成功，但点集无序性带来的计算成本仍是一个问题。本文另辟蹊径，探索从RGB-D输入生成抓取姿态。所提出的Keypoint-GraspNet方法在图像空间中检测夹爪关键点的投影，进而通过PnP算法恢复SE(3)位姿。我们基于基本形状和抓取族构建了一个合成数据集来验证这一思路。基于指标的评估表明，该方法在抓取提案准确性、多样性及时间成本方面均优于基线方法。最后的机器人实验展现了高成功率，验证了该思路在真实应用中的潜力。