The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms

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.

From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning

LiDAR-based 3D object detection datasets have been pivotal for autonomous driving, yet they cover a limited range of objects, restricting the model's generalization across diverse deployment environments. To address this, we introduce the first generalized cross-domain few-shot (GCFS) task in 3D object detection, which focuses on adapting a source-pretrained model for high performance on both common and novel classes in a target domain with few-shot samples. Our solution integrates multi-modal fusion and contrastive-enhanced prototype learning within one framework, holistically overcoming challenges related to data scarcity and domain adaptation in the GCFS setting. The multi-modal fusion module utilizes 2D vision-language models to extract rich, open-set semantic knowledge. To address biases in point distributions across varying structural complexities, we particularly introduce a physically-aware box searching strategy that leverages laser imaging principles to generate high-quality 3D box proposals from 2D insights, enhancing object recall. To effectively capture domain-specific representations for each class from limited target data, we further propose a contrastive-enhanced prototype learning, which strengthens the model's adaptability. We evaluate our approach with three GCFS benchmark settings, and extensive experiments demonstrate the effectiveness of our solution for GCFS tasks. The code will be publicly available.