Grasp estimation is a fundamental technique crucial for robot manipulation tasks. In this work, we present a scene-oriented grasp estimation scheme taking constraints of the grasp pose imposed by the environment into consideration and training on samples satisfying the constraints. We formulate valid grasps for a parallel-jaw gripper as vectors in a two-dimensional (2D) image and detect them with a fully convolutional network that simultaneously estimates the vectors’ origins and directions. The detected vectors are then converted to 6 degree-of-freedom (6-DOF) grasps with a tailored strategy. As such, the network is able to detect multiple grasp candidates from a cluttered scene in one shot using only an RGB image as input. We evaluate our approach on the GraspNet-1Billion dataset and archived comparable performance as state-of-the-art while being efficient in runtime.

抓取估计是对机器人操作任务至关重要的一项基本技术。在这项工作中，我们提出了一种面向场景的抓握估计方案，考虑到环境施加的抓握姿势的约束，并对满足约束的样本进行训练。我们将平行颚爪的有效抓握公式化为二维 (2D) 图像中的向量，并使用全卷积网络同时估计向量的原点和方向来检测它们。然后使用量身定制的策略将检测到的向量转换为 6 自由度 (6-DOF) 抓取。因此，网络能够仅使用 RGB 图像作为输入，在一次拍摄中从杂乱场景中检测到多个候选抓握。