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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16749 |
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| _version_ | 1866915765371797504 |
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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, JungHyun Park, JeongHyun Lee, Dongwoo Kim, Yeongmin Hwang, Myun Joong Kuribayashi, Yusuke Hiratsuka, Naoki Tanaka, Daisuke Arnold, Solvi Yamazaki, Kimitoshi Mateo-Agullo, Carlos Verleysen, Andreas Wyffels, Francis |
| author_facet | 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, JungHyun Park, JeongHyun Lee, Dongwoo Kim, Yeongmin Hwang, Myun Joong Kuribayashi, Yusuke Hiratsuka, Naoki Tanaka, Daisuke Arnold, Solvi Yamazaki, Kimitoshi Mateo-Agullo, Carlos Verleysen, Andreas Wyffels, Francis |
| contents | 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 diverse teams participated in the competition, utilizing our publicly released dataset of real-world robotic cloth unfolding attempts and a variety of methods to design their unfolding approaches. Afterwards, we also expanded our dataset with 176 competition evaluation trials, resulting in a dataset of 679 unfolding demonstrations across 34 garments. 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. The associated dataset is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. We hope that our benchmark, dataset and competition results can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation. The dataset and benchmarking code are available at https://airo.ugent.be/cloth_competition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 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, JungHyun Park, JeongHyun Lee, Dongwoo Kim, Yeongmin Hwang, Myun Joong Kuribayashi, Yusuke Hiratsuka, Naoki Tanaka, Daisuke Arnold, Solvi Yamazaki, Kimitoshi Mateo-Agullo, Carlos Verleysen, Andreas Wyffels, Francis Robotics 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 diverse teams participated in the competition, utilizing our publicly released dataset of real-world robotic cloth unfolding attempts and a variety of methods to design their unfolding approaches. Afterwards, we also expanded our dataset with 176 competition evaluation trials, resulting in a dataset of 679 unfolding demonstrations across 34 garments. 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. The associated dataset is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. We hope that our benchmark, dataset and competition results can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation. The dataset and benchmarking code are available at https://airo.ugent.be/cloth_competition. |
| title | A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.16749 |