Text-guided RGB-P grasp generation.

ABSTRACT: In the field of robotics, object grasping is a complex and challenging task. Although state-of-the-art computer vision-based models have made significant progress in predicting grasps, the lack of semantic information from textual data makes them susceptible to ambiguities in object recognition. For example, when asked to grasp a specific object on a table with many objects, robots relying only on visual data can easily get confused and grasp the wrong object. To address this limitation, we propose a multimodal approach that seamlessly integrates 3D data (shape) and red-green-blue (RGB) images (color, texture) into a unified representation called red-green-blue and point cloud (RGB-P), while also incorporating semantic information from textual descriptions processed by a large language mod

SUBMITTER: Vu VD 

PROVIDER: S-EPMC12453790 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature