TL;DR
A novel 7-DoF grasping pose generation framework that integrates sparse attention and null convolution is introduced, which enhances the model’s ability to capture fine-grained features from point clouds, significantly improving the accuracy of parallel gripping pose estimation.
Experiments show that M-VTOP achieves sub-millimeter accuracy under complex geometries, occlusions, and tight tolerances, demonstrating its promise for high-precision robotic manipulation.
An iterative closed-loop optimization framework that deeply couples SAM with FoundationPose and designs a multi-dimensional confidence assessment module that integrates both the 2D image domain and the 3D geometric domain to comprehensively evaluate the reliability of the current pose.
This paper studies monocular 6D pose estimation of small cubic objects from a single RGB image and proposes a two-stage manipulation- oriented framework, which achieves the strongest overall balance in ADD-S, translation accuracy, rotation stability, and task-oriented usability metrics.
A reinforcement learning-based framework for robotic grasp refinement, integrating keypoint-based object representations with a Deep Q-Network (DQN), is proposed, offering a scalable and adaptable solution for contact-rich manipulation tasks.
This study investigates in-hand pose estimation of a USB stick that is already held within a robotic gripper and provides a controlled task-specific analysis showing that structured model selection can improve multimodal grasp-pose regression within the evaluated setup.
An innovative approach is introduced for the random bin-picking of planar objects by developing a multi-task model for instance segmentation and keypoint detection in 2D images and a grasp candidate selection strategy is proposed to enable reliable grasping in cluttered industrial environments.