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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.
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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.
An aging population puts a pressure on health-care workers working with dementia patients globally. A potential solution is to provide care with Socially Assistive Robots (SARs), i.e. robots who help people through social interaction. However, for effective care these SARs must be able to personalize their behavior to individual patients and adapt this behavior to changes in this patient’s references. This paper presents the decision making process of a SAR that enables the SAR to personalize its behavior to the personality of the patients and adapt this behavior to their current state-of-mind. The system consists of a Fuzzy Logic Control personalization module, which personalizes the SAR’s behavior and a Reinforcement Learning based decision making module together with a Fuzzy Logic Control reward module for the adaption of the personalized behavior. The personalization of the SAR’s behavior is assessed by comparing the output of the system with answers from a survey. The average scatter index over all different behavioral parameters of the SAR is 20.4% The adaptation of the behavior is assessed with computer-based simulations, where an overall accuracy of 81.8% is achieved. A third experiment is carried out to assess the effect of adding the Fuzzy Logic Control personalization module to the system. This experiment shows that adding the personalization module to the decision making system of the SAR decreases the time for the learning process to converge with 13.3%. Although the first assessments of the system look promising, more extensive experiments should be held in later stages of the research. A crucial experiment that must be held in future research is performing real-life interactions between dementia patients and the SAR, in such an experiment the functionality of the system really can be assessed.
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An aging population puts a pressure on health-care workers working with dementia patients globally. A potential solution is to provide care with Socially Assistive Robots (SARs), i.e. robots who help people through social interaction. However, for effective care these SARs must be able to personalize their behavior to individual patients and adapt this behavior to changes in this patient’s references. This paper presents the decision making process of a SAR that enables the SAR to personalize its behavior to the personality of the patients and adapt this behavior to their current state-of-mind. The system consists of a Fuzzy Logic Control personalization module, which personalizes the SAR’s behavior and a Reinforcement Learning based decision making module together with a Fuzzy Logic Control reward module for the adaption of the personalized behavior. The personalization of the SAR’s behavior is assessed by comparing the output of the system with answers from a survey. The average scatter index over all different behavioral parameters of the SAR is 20.4% The adaptation of the behavior is assessed with computer-based simulations, where an overall accuracy of 81.8% is achieved. A third experiment is carried out to assess the effect of adding the Fuzzy Logic Control personalization module to the system. This experiment shows that adding the personalization module to the decision making system of the SAR decreases the time for the learning process to converge with 13.3%. Although the first assessments of the system look promising, more extensive experiments should be held in later stages of the research. A crucial experiment that must be held in future research is performing real-life interactions between dementia patients and the SAR, in such an experiment the functionality of the system really can be assessed.
