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 atthis https URL.
View on arXiv@article{gong2025_2409.13244,
title={ From Cognition to Precognition: A Future-Aware Framework for Social Navigation },
author={ Zeying Gong and Tianshuai Hu and Ronghe Qiu and Junwei Liang },
journal={arXiv preprint arXiv:2409.13244},
year={ 2025 }
}