Robots Ask the Way: Communication-Enabled Social Navigation

Valentino Sacco, Luca Scofano, Ludovica Mazza, Indro Spinelli, Fabio Galasso

Keywords: Embodied AI, Simulator, Social Navigation

Abstract:

Assistive autonomous robots operating in multi-agent environments require efficient strategies to locate specific individuals among multiple residents. Current social navigation methods focus on reactive collision avoidance and trajectory adaptation, but lack mechanisms to proactively gather information through human-robot communication.

We introduce Communication-enabled Social Navigation (CommNav). In this novel task, robotic agents actively seek assistance from residents to locate target individuals by requesting information about recent sightings, locations, and movement patterns.
To evaluate CommNav, we extend Habitat 3.0 to create Habitat 3.0c, a communication-enabled variant supporting multi-human environments with structured information exchange protocols. Adding COMM to a state-of-the-art social navigation model yields an improvement of 7\% in finding a specific individual and 8\% in overall episode success.

Our experiments reveal that: (i) explicit human-robot communication substantially enhances multi-person navigation performance; (ii) pre-training COMM on a communication pretext task effectively addresses the challenge of occasional interaction signals.

Primary Area: applications to robotics, autonomy, planning

Submission Number: 9461