Robotic Grasping and Manipulation Competition at the 2024 IEEE/RAS International Conference on Robotics and Automation [Competitions]

The Ninth Robotic Grasping and Manipulation Competition (RGMC) took place in Yokohama, Japan, during the 2024 IEEE/RAS International Conference on Robotics and Automation (ICRA). The series of RGMC events started in 2016 at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) with strong support from the conference’s organization committee, and since then they have been held each year at ICRA or IROS [1]. Across the editions, RGMC engaged the community in solving the open challenges associated with various robotic grasping and manipulation tasks for manufacturing, service robots, and logistics, and in advancing research and technology towards more realistic scenarios that can be encountered in daily activities at home or in warehouses. These tasks include assembling and disassembling boards, hand-in-hand grasping, picking and placing various objects, pouring liquids into a cup, bin picking, rearranging and setting formal tables, folding and unfolding cloths, and receiving objects handed over by a person. The goal of RGMC across these tasks is to assess the autonomous manipulation capabilities of a robotic arm when dealing with unknown or novel objects with varying physical properties and when handling scenarios with various degrees of uncertainty caused by a cluttered scene, random initial configurations, or human behaviors when interacting with the robot. For example, objects can vary in their shapes, appearances, transparency, deformability, and weight.

Authors

Publication Details

Journal
IEEE Robotics & Automation Magazine
Published
2024-12-01
DOI
https://doi.org/10.1109/mra.2024.3481609
Citations
2
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.66

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article

Robotic Grasping and Manipulation Competition at the 2024 IEEE/RAS International Conference on Robotics and Automation [Competitions]

Victor-Louis De Gusseme, Andrea Cavallaro, Berk Çallı, Kaiyu Hang et al.
2 citations
IEEE Robotics & Automation Magazine
Robot Manipulation and Learning
0.66
article

Robotic Grasping and Manipulation Competition at the 2024 IEEE/RAS International Conference on Robotics and Automation [Competitions]

Victor-Louis De Gusseme, Andrea Cavallaro, Berk Çallı, Kaiyu Hang, Salvatore D’Avella, Máximo A. Roa, Anastasia Mavrommati, Kenneth Kimble, Alessio Xompero, Francis wyffels, Yu Sun, José Roberto Reyes Avendaño
article en
2 citations

Abstract

The Ninth Robotic Grasping and Manipulation Competition (RGMC) took place in Yokohama, Japan, during the 2024 IEEE/RAS International Conference on Robotics and Automation (ICRA). The series of RGMC events started in 2016 at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) with strong support from the conference’s organization committee, and since then they have been held each year at ICRA or IROS [1]. Across the editions, RGMC engaged the community in solving the open challenges associated with various robotic grasping and manipulation tasks for manufacturing, service robots, and logistics, and in advancing research and technology towards more realistic scenarios that can be encountered in daily activities at home or in warehouses. These tasks include assembling and disassembling boards, hand-in-hand grasping, picking and placing various objects, pouring liquids into a cup, bin picking, rearranging and setting formal tables, folding and unfolding cloths, and receiving objects handed over by a person. The goal of RGMC across these tasks is to assess the autonomous manipulation capabilities of a robotic arm when dealing with unknown or novel objects with varying physical properties and when handling scenarios with various degrees of uncertainty caused by a cluttered scene, random initial configurations, or human behaviors when interacting with the robot. For example, objects can vary in their shapes, appearances, transparency, deformability, and weight.

IEEE Robotics & Automation MagazineVol. 31(4)
Canadian Institute for Advanced Research, Amazon Robotics
Openalex Percentile: Top 28%
Robot Manipulation and Learning
0.66
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