Exploiting Proximity-Aware Tasks for Embodied Social Navigation

Understand

Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agent to be integrated into our society.

• In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximity Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors.
• To this end, our tasks exploit the notion of immediate and future dangers of collision.
• Furthermore, we propose an evaluation protocol specifically designed for the Social Navigation Task in simulated environments.