OS17-5 Robotic Grasping of Common Objects: Focusing on Edge Detection for Improved Handling

Title:	OS17-5 Robotic Grasping of Common Objects: Focusing on Edge Detection for Improved Handling
Publication:	ICAROB2025
Volume:	30
Pages:	487-490
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2025.OS17-5
Author(s):	Tomoya Shiba, Hakaru Tamukoh
Publication Date:	February 13, 2025
Keywords:	Object recognition, Dataset, Service robot, Mobile manipulator, RoboCup@Home
Abstract:	Grasping objects like plates and cups poses unique challenges for robots because of their irregular shapes and the difficulty of finding reliable grasp points. Traditional approaches often attempt to grasp the object at its center, but this strategy tends to fail for items like plates or cups, whose shapes deviate from simple forms like cubes or spheres. To address this issue, we propose a new method that utilizes AI-powered image analysis to identify the best edges for grasping. Through experiments conducted with a home service robot and a set of YCB objects, we evaluated the effectiveness of our approach compared to conventional methods. The results revealed a significant improvement in the success rate, particularly for objects with prominent edges, such as cups.
PDF File:	https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS17/OS17-5.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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