From Cognition to Precognition: A Future-Aware Framework for Social Navigation

• URL: http://arxiv.org/abs/2409.13244v1
• Date: Fri, 20 Sep 2024 06:08:24 GMT
• Title: From Cognition to Precognition: A Future-Aware Framework for Social Navigation
• Authors: Zeying Gong, Tianshuai Hu, Ronghe Qiu, Junwei Liang,
• Abstract summary: We propose a reinforcement learning architecture, namely Falcon, to tackle socially-aware navigation. We introduce a novel SocialNav benchmark containing two new datasets, Social-HM3D and Social-MP3D. We conduct a detailed experimental analysis with the state-of-the-art learning-based method and two classic rule-based path-planning algorithms.
• Score: 1.9094009409000596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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