通过预判碰撞风险提升机器人社交导航能力，实现在人群中的安全通行。
Learning to Navigate Socially Through Proactive Risk Perception
- 基于Falcon模型加入主动风险感知模块，预测周围人的碰撞风险值。
- 在动态人群环境中保持个人空间，实现2025年IROS挑战赛第二名。
- 适用于需要自主避障与社交合规的室内服务机器人场景。
本文介绍了我们参加IROS 2025 RoboSense挑战赛社交导航赛道的技术细节。该赛道旨在开发基于RGB-D的感知与导航系统，使自主代理在动态人类密集的室内环境中安全、高效且符合社交规范地导航。挑战要求代理从第一人称视角仅使用机载传感器（包括RGB-D观测和里程计）运行，无法访问全局地图或特权信息，同时需遵守安全距离等社交规范。我们在Falcon模型基础上引入主动风险感知模块，学习预测周围人类的距离相关碰撞风险评分，从而增强代理的空间感知能力和主动避障行为。在Social-HM3D基准上的评估表明，我们的方法显著提升了代理在拥挤室内场景中维持个人空间的能力，实现2025年挑战赛16支队伍中的第二名。
原文摘要 · Abstract (English)
In this report, we describe the technical details of our submission to the IROS 2025 RoboSense Challenge Social Navigation Track. This track focuses on developing RGBD-based perception and navigation systems that enable autonomous agents to navigate safely, efficiently, and socially compliantly in dynamic human-populated indoor environments. The challenge requires agents to operate from an egocentric perspective using only onboard sensors including RGB-D observations and odometry, without access to global maps or privileged information, while maintaining social norm compliance such as safe distances and collision avoidance. Building upon the Falcon model, we introduce a Proactive Risk Perception Module to enhance social navigation performance. Our approach augments Falcon with collision risk understanding that learns to predict distance-based collision risk scores for surrounding humans, which enables the agent to develop more robust spatial awareness and proactive collision avoidance behaviors. The evaluation on the Social-HM3D benchmark demonstrates that our method improves the agent's ability to maintain personal space compliance while navigating toward goals in crowded indoor scenes with dynamic human agents, achieving 2nd place among 16 participating teams in the challenge.
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