Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots
This work addresses the need for socially appropriate robot behavior in domains like healthcare and education, though it is incremental as it builds on existing navigation and dialogue capabilities.
The paper tackles the problem of enabling social robots to adapt their navigation and conversational behavior based on user politeness cues, resulting in a dialogue system that dynamically modulates robot responses and behaviors in indoor environments, as tested with the Pepper robot.
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.