6 DoF Grasp

6-DoF grasp detection aims to identify optimal grasp poses for robotic manipulation, specifying both position and orientation in three-dimensional space. Current research emphasizes improving the accuracy and efficiency of grasp detection in cluttered environments, often employing deep learning models like convolutional neural networks and diffusion models, sometimes incorporating multimodal guidance (e.g., language, visual cues) or tactile feedback to enhance robustness and adaptability. These advancements are crucial for enabling robots to perform complex manipulation tasks in unstructured settings, impacting fields such as warehouse automation, assistive robotics, and household robotics.