Robot Visual Servoing Grasping Based on Top-Down Keypoint Detection Network

The paradigm of "deep-learning visual perception + hand-eye transformation + motion planning" for robot grasping has demonstrated viable capabilities in specific scenarios. However, its further development faces challenges in handling complex and dynamic environments. This article proposes a keypoint detection network-driven visual servoing grasping framework. First, we develop an efficient two-stage keypoint detector to perform real-time inference of sparse image-plane features for the target. Subsequently, a low-pass filtering algorithm is employed to smoothen the detected keypoints. These processed keypoints are then used in an image-based visual servoing (IBVS) controller to calculate the robot joint velocities, enabling precise tracking. A specialized dataset for training and evaluation was constructed using domain randomization techniques, comprising 11 K samples across six categories. Comprehensive experiments demonstrate the detector's low latency and accurate performance, even in low lighting, overexposure, partial occlusion, and densely packed environments. Static and dynamic grasping experiments validate that this framework achieves localization accuracy superior to five pixels and an overall grasping success rate exceeding 70% under unknown hand-eye calibration. The dataset is provided at github.com/hijunqi/VS_grasping_keypoint_detection_dataset.