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.

翻译：自主系统正日益部署于开放和动态的环境中——从城市街道到空中及室内空间——在这些环境中，感知模型必须在传感器噪声、环境变化和平台切换下保持可靠。然而，即使是最先进的方法在未见条件下也常常性能下降，这凸显了对鲁棒且可泛化的机器人感知的需求。RoboSense 2025挑战赛旨在提升机器人感知在多样化传感场景下的鲁棒性和适应性。它整合了五个互补的研究赛道，涵盖语言驱动的决策制定、社会合规导航、传感器配置泛化、跨视角与跨模态对应，以及跨平台三维感知。这些任务共同构成了一个全面的基准，用于评估在领域偏移、传感器故障和平台差异下的真实世界感知可靠性。RoboSense 2025提供了标准化的数据集、基线模型和统一的评估协议，使得能够对鲁棒感知方法进行大规模且可复现的比较。本次挑战赛吸引了来自16个国家、85个机构的143支队伍参与，反映了广泛的社区参与度。通过整合来自23个获胜解决方案的见解，本报告突出了各赛道中新兴的方法学趋势、共享的设计原则以及开放挑战，标志着朝着构建能够在真实世界环境中可靠感知、鲁棒行动并跨平台自适应的机器人迈出了一步。