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

2026-01-08

AI Summary

This work addresses the challenges of robotic perception in open, dynamic environments—such as sensor noise, environmental variability, and platform heterogeneity—by introducing the first unified benchmark framework that integrates multidimensional tasks. The framework encompasses five core tracks: language-guided decision-making, socially compliant navigation, sensor configuration generalization, cross-view/cross-modal alignment, and cross-platform 3D perception. Through standardized datasets and evaluation protocols, it synergistically combines techniques in language-vision alignment, multimodal fusion, domain adaptation, and social behavior modeling to advance reproducible and robust perception research. The initiative attracted participation from 143 teams across 85 institutions in 16 countries, with 23 winning solutions yielding common methodologies and design principles that significantly accelerate progress in cross-platform robust perception.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

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.

Problem

• robust perception
• domain shift
• sensor noise
• platform generalization
• environmental variation

Innovation

• robust perception
• cross-platform adaptation
• domain generalization
• multimodal correspondence
• autonomous navigation