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

TL;DR

The RoboSense 2025 Challenge aims to advance robust and adaptable perception in autonomous systems across diverse environments by providing standardized benchmarks, datasets, and evaluation protocols, fostering broad community engagement and innovation.

Contribution

This paper introduces the RoboSense 2025 Challenge, a comprehensive benchmark with multiple research tracks, datasets, and evaluation methods to improve perception robustness and adaptability in robotics.

Findings

143 teams participated from 85 institutions

23 winning solutions reveal emerging trends and shared principles

Benchmark highlights open challenges in robust robot sensing

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.