From Cognition to Precognition: A Future-Aware Framework for Social Navigation
This addresses the problem of safe and efficient robot navigation in crowded indoor environments for robotics applications, representing an incremental improvement with a novel benchmark.
The paper tackles socially-aware robot navigation by proposing a reinforcement learning framework that predicts human trajectories to avoid blocking future paths, achieving a 55% task success rate and 90% personal space compliance on a new benchmark.
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/ .