RoboSenseチャレンジ：あらゆる環境を認識し、どこへでも移動し、プラットフォームを超えて適応する
The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms
ロボット知覚の堅牢性と適応性を競うRoboSense 2025チャレンジの報告で、5つのタスクのベンチマークと23の受賞解法から得られた知見をまとめた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Lingdong Kong, Shaoyuan Xie, Zeying Gong, Ye Li, Meng Chu, Ao Liang, Yuhao Dong, Tianshuai Hu, Ronghe Qiu, Rong Li, Hanjiang Hu, Dongyue Lu, Wei Yin, Wenhao Ding, Linfeng Li, Hang Song, Wenwei Zhang, Yuexin Ma, Junwei Liang, Zhedong Zheng, Lai Xing Ng, Benoit R. Cottereau, Wei Tsang Ooi, Ziwei Liu, Zhanpeng Zhang, Weichao Qiu, Wei Zhang, Ji Ao, Jiangpeng Zheng, Siyu Wang, Guang Yang, Zihao Zhang, Yu Zhong, Enzhu Gao, Xinhan Zheng, Xueting Wang, Shouming Li, Yunkai Gao, Siming Lan, Mingfei Han, Xing Hu, Dusan Malic, Christian Fruhwirth-Reisinger, Alexander Prutsch, Wei Lin, Samuel Schulter, Horst Possegger, Linfeng Li, Jian Zhao, Zepeng Yang, Yuhang Song, Bojun Lin, Tianle Zhang, Yuchen Yuan, Chi Zhang, Xuelong Li, Youngseok Kim, Sihwan Hwang, Hyeonjun Jeong, Aodi Wu, Xubo Luo, Erjia Xiao, Lingfeng Zhang, Yingbo Tang, Hao Cheng, Renjing Xu, Wenbo Ding, Lei Zhou, Long Chen, Hangjun Ye, Xiaoshuai Hao, Shuangzhi Li, Junlong Shen, Xingyu Li, Hao Ruan, Jinliang Lin, Zhiming Luo, Yu Zang, Cheng Wang, Hanshi Wang, Xijie Gong, Yixiang Yang, Qianli Ma, Zhipeng Zhang, Wenxiang Shi, Jingmeng Zhou, Weijun Zeng, Kexin Xu, Yuchen Zhang, Haoxiang Fu, Ruibin Hu, Yanbiao Ma, Xiyan Feng, Wenbo Zhang, Lu Zhang, Yunzhi Zhuge, Huchuan Lu, You He, Seungjun Yu, Junsung Park, Youngsun Lim, Hyunjung Shim, Faduo Liang, Zihang Wang, Yiming Peng, Guanyu Zong, Xu Li, Binghao Wang, Hao Wei, Yongxin Ma, Yunke Shi, Shuaipeng Liu, Dong Kong, Yongchun Lin, Huitong Yang, Liang Lei, Haoang Li, Xinliang Zhang, Zhiyong Wang, Xiaofeng Wang, Yuxia Fu, Yadan Luo, Djamahl Etchegaray, Yang Li, Congfei Li, Yuxiang Sun, Wenkai Zhu, Wang Xu, Linru Li, Longjie Liao, Jun Yan, Benwu Wang, Xueliang Ren, Xiaoyu Yue, Jixian Zheng, Jinfeng Wu, Shurui Qin, Wei Cong, Yao He
分類: cs.RO
原文アブストラクト
Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under sensor noise, environmental variation, and platform shifts. However, even state-of-the-art methods often degrade under unseen conditions, highlighting the need for robust and generalizable robot sensing. The RoboSense 2025 Challenge is designed to advance robustness and adaptability in robot perception across diverse sensing scenarios. It unifies five complementary research tracks spanning language-grounded decision making, socially compliant navigation, sensor configuration generalization, cross-view and cross-modal correspondence, and cross-platform 3D perception. Together, these tasks form a comprehensive benchmark for evaluating real-world sensing reliability under domain shifts, sensor failures, and platform discrepancies. RoboSense 2025 provides standardized datasets, baseline models, and unified evaluation protocols, enabling large-scale and reproducible comparison of robust perception methods. The challenge attracted 143 teams from 85 institutions across 16 countries, reflecting broad community engagement. By consolidating insights from 23 winning solutions, this report highlights emerging methodological trends, shared design principles, and open challenges across all tracks, marking a step toward building robots that can sense reliably, act robustly, and adapt across platforms in real-world environments.
