Intent-Aware Backchanneling for Active Listening in Human-Robot Interaction

Abstract

In conversational Human-Robot Interaction, robots typically remain silent during user speech and reply only after a pause, making interaction feel unnatural. In contrast, humans signal that they listen through active behavior. To overcome this, we present a system in which a social robot conveys active listening through non-verbal backchannels grounded in interactional intents. The system combines two ideas: (i) a dual-stage framework separating the user’s communicative intent (Speaker Intent) from the robot’s interactional stance (Listener Intent), mapping the latter to non-verbal reactions; and (ii) a parallel pipeline whose chunk-level branch generates non-verbal feedback during speech while a turn-level branch produces the verbal reply at turn end. We deployed this system on a robot and conducted a usability study (N = 6) in a hotel-negotiation task. We found that participants considered the system usable and could interpret gestures. We contribute a ready-to-deploy intent-aware system to enable active listening for robots.