LU4R: Adaptive Spoken Language Understanding for Robots

Abstract

Service robots are expected to operate in specific environments, where the presence of humans plays a key role. It is thus essential to enable for a natural and effective communication among humans and robots. One of the main features of such robotics platforms is the ability to react to spoken commands. This requires a comprehensive understanding of the user utterance to trigger the robot reaction. Moreover, the correct interpretation of linguistic interactions depends on physical, cognitive and language-dependent aspects related to the environment. In this work, we present the latest version of LU4R - adaptive spoken Language Understanding 4 Robots, a Spoken Language Understanding framework for the semantic interpretation of robotic commands, that is sensitive to the operational environment. The overall system is designed according to a Client/Server architecture in order to be easily deployed in a vast plethora of robotic platforms. Moreover, an improved version of HuRIC - Human-Robot Interaction Corpus is presented. The main novelty presented in this paper is the extension to commands expressed in Italian. In order to prove the effectiveness of such system, we also present some empirical results in both English and Italian computed over the new HuRIC resource.