Abstract
Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we organized the ICRA 2024 Cloth Competition, a unique head-to-head evaluation focused on grasp pose selection for cloth unfolding. Eleven diverse teams competed with a shared dual-arm robot, utilizing our publicly released dataset of real-world robotic cloth unfolding attempts. We expanded this dataset with 176 live evaluation trials, which now encompasses 679 unfolding demonstrations across 34 garments. The competition established a key benchmark and reference for robotic cloth manipulation. Analysis revealed a significant discrepancy between competition performance and prior work, underscoring the importance of independent out-of-the-lab evaluation in robotic cloth manipulation. The resulting dataset, one of the most comprehensive collections of real-world robotic cloth manipulation data, is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. It can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation.
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BibTeX (Download)
@online{DeGusseme2024BenchmarkingGrasp,
title = {A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition},
author = {Victor-Louis De Gusseme and Thomas Lips and Remko Proesmans and Julius Hietala and Giwan Lee and Jiyoung Choi and Jeongil Choi and Geon Kim and Phayuth Yonrith and Domen Tabernik and Andrej Gams and Peter Nimac and Matej Urbas and Jon Muhovic and Danijel Skocaj and Matija Mavsar and Hyojeong Yu and Minseo Kwon and Young J. Kim and Yang Cong and Ronghan Chen and Yu Ren and Supeng Diao and Jiawei Weng and Jiayue Liu and Haoran Sun and Linhan Yang and Zeqing Zhang and Ning Guo and Lei Yang and Fang Wan and Chaoyang Song and Jia Pan and Yixiang Jin and Yong A and Jun Shi and Dingzhe Li and Yong Yang and Kakeru Yamasaki and Takumi Kajiwara and Yuki Nakadera and Krati Saxena and Tomohiro Shibata and Chongkun Xia and Kai Mo and Yanzhao Yu and Qihao Lin and Binqiang Ma and Uihun Sagong and JungHyun Choi and JeongHyun Park and Dongwoo Lee and Yeongmin Kim and Myun Joong Hwang and Yusuke Kuribayashi and Naoki Hiratsuka and Daisuke Tanaka and Solvi Arnold and Kimitoshi Yamazaki and Carlos Mateo-Agullo and Andreas Verleysen and Francis wyffels},
url = {https://airo.ugent.be/cloth_competition/},
year = {2025},
date = {2025-01-10},
abstract = {Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we organized the ICRA 2024 Cloth Competition, a unique head-to-head evaluation focused on grasp pose selection for cloth unfolding. Eleven diverse teams competed with a shared dual-arm robot, utilizing our publicly released dataset of real-world robotic cloth unfolding attempts. We expanded this dataset with 176 live evaluation trials, which now encompasses 679 unfolding demonstrations across 34 garments. The competition established a key benchmark and reference for robotic cloth manipulation. Analysis revealed a significant discrepancy between competition performance and prior work, underscoring the importance of independent out-of-the-lab evaluation in robotic cloth manipulation. The resulting dataset, one of the most comprehensive collections of real-world robotic cloth manipulation data, is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. It can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation.},
note = {Submitted to The International Journal of Robotics Research},
keywords = {Authorship - Co-Author, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
