Stable Robotic Grasping with Center-Guided Grasp Pose Estimation
Deng JR(邓杰仁)1,2; Hu JH(胡建华)1; Zhang HJ(张好剑)1; Wang YK(王云宽)1
2023
Conference Name2023 8th International Conference on Control, Robotics and Cybernetics (CRC)
Conference Date2023-12-22
Conference Place中国长沙
Abstract

Robotic grasp pose estimation in unstructured environments remains a critical challenge in the field of robotics. Existing methods, like GraspNet, while effective, often overlook pivotal aspects such as an object’s weight distribution and inherent frictional forces. This oversight can lead to unstable grasping, especially evident when handling delicate items. To address this, this paper introduces a novel approach: center-guided grasp pose estimation. By leveraging deep learning techniques, the proposed method predicts an object’s center even in scenarios where point clouds from a single viewpoint are incomplete. This methodology prioritizes the object’s center, promoting more stable and precise grasps. Preliminary results indicate a marked improvement in grasp quality, underscoring the potential for safer and more effective robotic interactions in complex environments.

Indexed ByEI
Sub direction classification智能机器人
planning direction of the national heavy laboratory实体人工智能系统感认知
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57376
Collection中科院工业视觉智能装备工程实验室_先进制造与自动化
Corresponding AuthorWang YK(王云宽)
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Deng JR,Hu JH,Zhang HJ,et al. Stable Robotic Grasping with Center-Guided Grasp Pose Estimation[C],2023.
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