Using Circular Smooth Label in Robotic Grasp Detection for Orientation Boundary Discontinuity

Robotic grasping is essential for intelligent robot operation. The performance of grasp detection relies on the measurement of the grasp rectangle's position and orientation. The rotation detector based on regression, however, faces the issue of boundary discontinuity as a result of the periodicity of the angle. This boundary discontinuity issue may make it difficult for the loss function to determine the precise grasp direction at the angle around the discontinuity. By comparing the differences between the conventional regression-based rotation, one-hot based rotation, and the CSL-based rotation in loss calculation, we introduce a circular smooth label (CSL) strategy to address the orientation boundary discontinuity problem and increase the error tolerance to adjacent angles. Based on the Yolov5 framework, we put our suggested approach into practice and ran tests on the Cornell grasp datasets. The experiment results indicate that the CSL-YOLOv5 outperforms the existing grasping methods on grasp rectangle predictions.