Personalized Human-Robot Cognitive Interaction via a Novel Fuzzy Logic Control and Learning-Based Paradigm

Abstract

Socially assistive robotics is an emerging field that, through effective human-robot cognitive interactions (HRCIs), offers potential solutions for personalized care, education, and entertainment. For improved impact and for acceptance by humans, socially assistive robots (SARs) should autonomously personalize and adapt their behavior to, respectively, the personality and the changes in the states-of-mind of people they interact with. Despite extensive research on the ethical, societal, and psychological aspects of SARs, bridging systems-and-control-based methods and socially assistive robotics for developing control approaches that automate the personalization and adaptation of HRCIs remains under-attended. We propose the first systematic and generalizable paradigm for personalization and adaptation of the social interactive behaviors of SARs, combining two highly promising modeling and decision making approaches, namely fuzzy logic control (FLC) and reinforcement learning (RL). By replicating the rule-based decision making of humans, FLC provides a highly effective personalization mechanism and warm-starts the RL algorithm, which takes care of adapting the behaviors of SARs to the dynamics of people's state-of-mind. Fuzzy logic is also used to develop two consecutive processes inside the RL-based adaptation module that, from the emotional responses of humans, estimate their state-of-mind and assign a reward to the most recent action of the SAR. Our extensive experiments for validation of this combined paradigm and for comparing it with conventional RL methods show meaningful improvements in the criteria that assess the personalization, convergence of learning, and performance accuracy of the proposed steering system for SARs.