6 DoF Grasp Pose

6-DoF grasp pose estimation aims to determine the optimal orientation and position for a robotic gripper to securely grasp an object, a crucial step for autonomous manipulation. Current research heavily focuses on developing robust and generalizable methods using deep learning architectures like graph neural networks and convolutional neural networks, often incorporating implicit object representations and multi-level feature extraction to handle cluttered scenes and unseen objects. These advancements improve grasp success rates, particularly in challenging environments, and are driving progress towards more capable and adaptable robotic systems for various applications, including industrial automation and assistive robotics.