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

Zeying Gong, Tianshuai Hu, Ronghe Qiu, Junwei Liang

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.