Achieving a real-time precise grasp of a specified target object in densely cluttered environments is an essential capability for autonomous robot operation. Recently, considerable investigations on planar and spatial grasp have been carried out, and significant results have been obtained. However, these point cloud-based grasp prediction methods often fail to ensure that the generated grasp configurations meet the precise requirements of the task. Additionally, some of the existing grasp pipelines are too time-consuming to meet the demand for real-time robot response. In more challenging cluttered scenes, the quality of pose and gripper jaw opening estimation in highdimensional space requires further improvement. Therefore, this paper introduces a data- and model-independent and efficient method to generate 7-DoF grasp configurations for arbitrary target objects from single-view point cloud data in dense cluttered scenes. In addition, this paper proposes a grasp framework that generates the grasp configuration for the target object while reducing the time consumed during the grasp process, to enable robots to efficiently grasp target objects for designated tasks. The grasp pipeline focuses on guided regions via target detection and rapidly adjusts grasp configurations through multi-region point cloud distribution perception. Extensive real-world robot experiments have demonstrated the effectiveness of the proposed method in grasping target objects in cluttered scenes, achieving higher success rates and reduced runtime compared to baseline methods. The realized code and video are available at https://github.com/L-tj/7DGCG.

In recent years, Vision-Language Models (VLMs) have exhibited powerful capacity of reasoning, decomposing long-horizon tasks and motion planning in robotic manipulation tasks. However, the current operating speed of VLMs has limited the interaction frequency of users and the model to several seconds, which disables the real-time perception of environmental changes when executing tasks released by VLM. We propose Real-time Object Detection - VLM (ROD-VLM), a novel framework which combines classical Object Detection Algorithm YOLO-v5x with VLM to achieve the real-time robotic environmental perception, reasoning and manipulation. Specifically, we introduce the concept of key frame to VLM model, capturing the crucial information through object detection algorithm to assist VLM in perceiving varying environment. Our comprehensive real-world experiments show that ROD-VLM possess an excellent capability in real-time environmental understanding, decision-making and action executing.