Continuous multi-modal human interest detection for a domestic companion humanoid robot

This paper addresses the scenario of how a humanoid robot can detect its human companion´s intention of initiating interaction in a domestic environment. Previous research was mostly focused on the robot´s understanding of human intentions during the course of the interaction, but not the initiation of it. The performance of the several unimodal and multi-modal algorithms have been evaluated using over 100 hours of real life experiments conducted in a city apartment living room. A key evaluation criterion is the ability of the algorithm to continuously monitor its environments to be responsive to human´s infrequent requests, while at the same time having an extremely low number of false positives so as not to fire off interactions at random. The multi-modal approach incorporating speech recognition, audio source localisation and face detection features is proven to be superior, but further improvements are desired for real life applications.