让机器人提前预判人类动向，更安全地穿越人群。
From Cognition to Precognition: A Future-Aware Framework for Social Navigation
- 用强化学习显式预测人类未来轨迹，避免阻碍其路径
- 在新基准上达成55%任务成功率，90%保持个人空间
- 适合研究社交导航与智能机器人交互的开发者
为在密集环境中安全高效导航，机器人不仅需感知当前环境，还应预判人类未来行为。本文提出一种名为Falcon的强化学习架构，通过显式预测人类轨迹并惩罚可能阻挡其路径的动作，实现社交感知导航。为支持真实评估，我们构建了新的SocialNav基准，包含两个新数据集：基于场景面积合理分布人类代理的Photo-realistic Indoor Scenes（Social-HM3D与Social-MP3D），涵盖自然的人类运动与轨迹模式。我们在新基准上对前沿学习方法及两种经典规则基路径规划算法进行了详尽实验。结果表明未来预测至关重要，所提方法在任务成功率达55%的同时，保持约90%的个人空间合规率。代码与数据集将公开，演示视频见https://zeying-gong.github.io/projects/falcon/。
原文摘要 · Abstract (English)
To navigate safely and efficiently in crowded spaces, robots should not only perceive the current state of the environment but also anticipate future human movements. In this paper, we propose a reinforcement learning architecture, namely Falcon, to tackle socially-aware navigation by explicitly predicting human trajectories and penalizing actions that block future human paths. To facilitate realistic evaluation, we introduce a novel SocialNav benchmark containing two new datasets, Social-HM3D and Social-MP3D. This benchmark offers large-scale photo-realistic indoor scenes populated with a reasonable amount of human agents based on scene area size, incorporating natural human movements and trajectory patterns. We conduct a detailed experimental analysis with the state-of-the-art learning-based method and two classic rule-based path-planning algorithms on the new benchmark. The results demonstrate the importance of future prediction and our method achieves the best task success rate of 55% while maintaining about 90% personal space compliance. We will release our code and datasets. Videos of demonstrations can be viewed at https://zeying-gong.github.io/projects/falcon/ .
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