A Neuro-Fuzzy-Bayesian Approach for the Adaptive Control of Robot Proxemics Behavior

A robotic system that is designed to coexist with humans has to adapt its behavioral and social interaction parameters not only with respect to the task it is supposed to accomplish, but also with respect to the human being it is interacting with by profiling her habits, preferences, and personality. This is particularly relevant in the domain of assistive robotics where the behavioral adaptability has been shown to enhance the users' acceptability of a robot. In this work, we propose a neuro-fuzzy-bayesian system able to adapt the robot proxemics behavior with respect to the human users' personality and the action she is currently performing. The user's personality is evaluated according to the Big-Five factors model and the activity recognition is obtained by classifying data from a wearable device through the use of a Bayesian Network classifier. As shown by a statistical study, the proposed framework is capable of computing the most appropriate robot proxemics behavior in order to improve human feeling in interacting with artificial agents, such as robots