Grasp Pose

Grasp pose research focuses on determining the optimal orientation and position of a robotic gripper to successfully grasp an object. Current efforts concentrate on improving the robustness and generalization of grasp pose estimation, often employing deep learning models like convolutional neural networks and transformers, and incorporating diverse data sources such as RGB images, depth sensors, and tactile feedback. This field is crucial for advancing robotic manipulation capabilities in various applications, from warehouse automation and industrial settings to human-robot interaction and assistive technologies.