TL;DR
Experiments show that M-VTOP achieves sub-millimeter accuracy under complex geometries, occlusions, and tight tolerances, demonstrating its promise for high-precision robotic manipulation.
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.
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.
A unified monocular vision-based grasping framework that targets both soft and rigid objects within a single control pipeline, using only RGB input and a position-controlled gripper, and is validated in real-world pick-and-place experiments.
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.
FreeTrack6D, a training-free unified segmentation and 6D pose tracking framework integrating frame-wise mask generation, multi-hypothesis pose refinement, motion prediction, observation gating, and visual-servo-based robot control, forms a closed-loop training-free dynamic grasping framework.
The proposed PC-IMVL integrates deep visual perception with embedded physical modeling, which explicitly introduces spatial physical constraints into the network optimization process and builds a physical consistency-aware loss function to regularize 3D position and pose estimation, which enables efficient and reliable...
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