Handling uncertain input in multi-user human-robot interaction
Simon Keizer, Mary Ellen Foster, Andre Gaschler, Manuel Giuliani, Amy Isard, Oliver Lemon · 2014
In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection components in which state uncertainty is tracked and exploited, and compare it to a baseline version that uses standard speech recognition confidence score thresholds instead of belief tracking. The results suggest that users are served fewer incorrect drinks when the uncertainty is retained in the state.