A learning from demonstration system architecture for robots learning social group recreational activities

Group-based recreational activities have shown to have a number of health benefits for people of all ages. The handful of social robots designed to facilitate such activities are currently only able to implement a priori known recreational activities that have been pre-programmed by human experts. Once deployed in their intended facility, these robots are not able to learn new activities from non-expert humans. In this paper, we present the development of a novel learning from demonstration (LfD) system architecture for a social robot in order for it to learn from non-expert teachers the structure of an activity and monitor the execution of the new activity. In order to obtain user compliance, personalized persuasive strategies are also learned by the robot to use while implementing the activity during human-robot interactions (HRI) with the intended users. The architecture has been integrated into our socially assistive robot Tangy to learn the group-based activity Bingo. System performance experiments were conducted with Tangy to first learn to facilitate Bingo from non-expert teachers and then use the learned activity to physically facilitate Bingo with multiple users. The results showed Tangy was able to effectively and efficiently learn the new Bingo activity structure as well as personalize its persuasive strategies to individual users in order to obtain activity compliance.