Improving robotic grasping accuracy through oriented bounding box detection with YOLOv11-OBB

Abstract

<h2>Abstract</h2> This study presents the application of YOLOv11-OBB to the grasp detection problem. The YOLOv11-OBB model is commonly used in object detection problems which provides the parameters of the oriented bounding box, identical to the grasp parameters. Therefore, when applied to the grasping problem, it is only necessary to modify the parameters of the bounding box to represent an optimal grasp configuration in the annotating process. Furthermore, only a single label is used in the labeling process because the goal is only to determine the optimal grasp configuration, not to classify the objects. To evaluate the performance of YOLOv11-OBB in grasp detection, we conducted experiments not only on the Cornell Grasping Dataset but also on an expanded multi-object dataset, which includes 20 different object categories in various backgrounds. To further enhance generalization, we incorporated advanced data augmentation techniques, including shape deformation, rotation, cropping, and color transformation. Our results indicate that YOLOv11-OBB surpasses existing grasp detection models, including ResNet-50, AlexNet, GRPN, and GraspNet, in both accuracy and inference speed. The model achieves 98.5 % accuracy on the Cornell Grasping Dataset and maintains a grasp quality score exceeding 0.7 in multi-object scenarios. Furthermore, it demonstrates strong generalization capabilities, effectively detecting grasp configurations even for previously unseen objects. The proposed approach not only improves grasp detection performance but also ensures real-time feasibility, with an inference time of 29 ms, making it highly suitable for robotic applications in dynamic environments.