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A dataset and benchmark for robotic cloth unfolding grasp selection: The ICRA 2024 Cloth Competition

De Gusseme, Victor-Louis
; 
Lips, Thomas
; 
Proesmans, Remko
; 
Hietala, Julius
; 
Lee, Giwan
; 
Choi, Jiyoung
; 
Choi, Jeongil
; 
Kim, Geon
; 
Yonrith, Phayuth
; 
Tabernik, Domen
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Author
De Gusseme, Victor-Louis
Lips, Thomas
Proesmans, Remko
Hietala, Julius
Lee, Giwan
Choi, Jiyoung
Choi, Jeongil
Kim, Geon
Yonrith, Phayuth
Tabernik, Domen
Gams, Andrej
Nimac, Peter
Urbas, Matej
Muhovič, Jon
Skočaj, Danijel
Mavsar, Matija
Yu, Hyojeong
Kwon, Minseo
Kim, Young J
Cong, Yang
Chen, Ronghan
Ren, Yu
Diao, Supeng
Weng, Jiawei
Liu, Jiayue
Sun, Haoran
Yang, Linhan
Zhang, Zeqing
Guo, Ning
Yang, Lei
Wan, Fang
Song, Chaoyang
Pan, Jia
Jin, Yixiang
A, Yong
Shi, Jun
Li, Dingzhe
Yang, Yong
Yamasaki, Kakeru
Kajiwara, Takumi
Nakadera, Yuki
Saxena, Krati
Shibata, Tomohiro
Xia, Chongkun
Mo, Kai
Yu, Yanzhao
Lin, Qihao
Ma, Binqiang
Sagong, Uihun
Choi, Jung Hyun
Park, Jeong Hyun
Lee, Dongwoo
Kim, Yeongmin
Hwang, Myun Joong
Kuribayashi, Yusuke
Hiratsuka, Naoki
Tanaka, Daisuke
Arnold, Solvi
Yamazaki, Kimitoshi
Mateo-Agullo, Carlos
Verleysen, Andreas
wyffels, Francis
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Department
Robotics
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Journal article
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English
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Abstract
Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we created a benchmark and organized the ICRA 2024 Cloth Competition, a unique head-to-head evaluation focused on grasp pose selection for in-air robotic cloth unfolding. Eleven teams participated in the competition, utilizing the publicly released dataset of 500 real-world robotic grasp attempts for cloth unfolding and employing diverse approaches to generate in-air unfolding grasps. Analysis of the competition results revealed insights about the trade-off between grasp success and coverage, the surprisingly strong achievements of hand-engineered methods and a significant discrepancy between competition performance and prior work, underscoring the importance of independent, out-of-the-lab evaluation in robotic cloth manipulation. We also expanded the dataset with 176 competition evaluation trials, resulting in a dataset of 679 unfolding demonstrations across 34 garments. This dataset is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. We hope that the benchmark, dataset, and competition results can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation.
Citation
V.-L. De Gusseme, T. Lips, R. Proesmans, J. Hietala, G. Lee, J. Choi, J. Choi, G. Kim, et. al, "A dataset and benchmark for robotic cloth unfolding grasp selection: The ICRA 2024 Cloth Competition," The International Journal of Robotics Research, 2026, https://doi.org/10.1177/02783649251414885.
Source
The International Journal of Robotics Research
Conference
Keywords
46 Information and Computing Sciences, 4608 Human-Centred Computing
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Publisher
SAGE Publications
DOI
10.1177/02783649251414885
Additional links
https://journals.sagepub.com/doi/10.1177/02783649251414885
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