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Published:2025/8/22 19:03:37

ロボット、布をピシッと畳む!:ICRA2024布展開コンペで未来を掴め✨

超要約: ロボットが布をキレイに畳む技術! 物流や介護を激変させるかも!?

✨ ギャル的キラキラポイント ✨ ● 布の畳み方、ロボが学ぶ時代が来たってコト! ● データ公開&競技会で、みんなで技術を磨くの🔥 ● 最終的には、私たちの生活が超便利になるかも!

詳細解説いくよ~!

背景 ロボットって、固いものは得意だけど、布みたいにフニャフニャしたものは苦手だったの。でも、アパレルとか介護とか、布を扱う場面ってめっちゃ多いじゃん? だから、ロボットに布を上手に扱えるようにさせよう!って研究が進んでるんだって!

続きは「らくらく論文」アプリで

A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition

Victor-Louis De Gusseme / Thomas Lips / Remko Proesmans / Julius Hietala / Giwan Lee / Jiyoung Choi / Jeongil Choi / Geon Kim / Phayuth Yonrith / Domen Tabernik / Andrej Gams / Peter Nimac / Matej Urbas / Jon Muhovi\v{c} / Danijel Sko\v{c}aj / Matija Mavsar / Hyojeong Yu / Minseo Kwon / Young J. Kim / Yang Cong / Ronghan Chen / Yu Ren / Supeng Diao / Jiawei Weng / Jiayue Liu / Haoran Sun / Linhan Yang / Zeqing Zhang / Ning Guo / Lei Yang / Fang Wan / Chaoyang Song / Jia Pan / Yixiang Jin / Yong A / Jun Shi / Dingzhe Li / Yong Yang / Kakeru Yamasaki / Takumi Kajiwara / Yuki Nakadera / Krati Saxena / Tomohiro Shibata / Chongkun Xia / Kai Mo / Yanzhao Yu / Qihao Lin / Binqiang Ma / Uihun Sagong / JungHyun Choi / JeongHyun Park / Dongwoo Lee / Yeongmin Kim / Myun Joong Hwang / Yusuke Kuribayashi / Naoki Hiratsuka / Daisuke Tanaka / Solvi Arnold / Kimitoshi Yamazaki / Carlos Mateo-Agullo / Andreas Verleysen / Francis Wyffels

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.

cs / cs.RO