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.

翻译：本报告阐述了我们提交至IROS 2025 RoboSense挑战赛社会导航赛道的技术方案细节。该赛道聚焦于开发基于RGBD的感知与导航系统，使自主智能体能在动态人流量密集的室内环境中实现安全、高效且符合社会规范的导航。挑战要求智能体仅通过车载传感器（包括RGB-D观测与里程计）以第一人称视角运行，无法获取全局地图或特权信息，同时需保持符合社会规范的行为，如维持安全距离与避免碰撞。我们在Falcon模型基础上，引入了主动风险感知模块以提升社会导航性能。该方法通过增强Falcon对碰撞风险的理解能力，使其能够学习预测周围行人基于距离的碰撞风险评分，从而使智能体建立更鲁棒的空间感知能力与主动避撞行为。在Social-HM3D基准测试上的评估表明，我们的方法显著提升了智能体在充满动态行人的拥挤室内场景中朝向目标导航时维持个人空间合规性的能力，最终在参赛的16支队伍中荣获第二名。