Towards Dialogue-Based Navigation with Multivariate Adaptation Driven by Intention and Politeness for Social Robots

Abstract

Service robots need to show appropriate social behaviour in order to be deployed in social environments such as healthcare, education, retail, etc. Some of the main capabilities that robots should have are navigation and conversational skills. If the person is impatient, the person might want a robot to navigate faster and vice versa. Linguistic features that indicate politeness can provide social cues about a person's patient and impatient behaviour. The novelty presented in this paper is to dynamically incorporate politeness in robotic dialogue systems for navigation. Understanding the politeness in users' speech can be used to modulate the robot behaviour and responses. Therefore, we developed a dialogue system to navigate in an indoor environment, which produces different robot behaviours and responses based on users' intention and degree of politeness. We deploy and test our system with the Pepper robot that adapts to the changes in user's politeness.

Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU

“…The NLU problem has been studied also on the Interactive Robotics front, mostly to support basic dialogue systems, with few dialogue states and tailored for specific tasks, such as semantic mapping (Kruijff et al, 2007), navigation (Kollar et al, 2010;Bothe et al, 2018), or grounded language learning (Chai et al, 2016). However, the designed approaches, either based on formal languages or data-driven, have never been shown to scale to real world scenarios.…”