Inferring Human Feelings and Desires for Human-Robot Trust Promotion

Abstract

Trust is a key component in developing successful interpersonal relationships. In this paper, we posit that the same is true for Human-Robot Interaction (HRI), since human trust toward robots can facilitate HRI in terms of comfort and usability. We investigated the ability of a socially assistive robot to promote trust in the social relationship with its user by inducing self-disclosure of the user’s negative experiences and offering coping mechanisms to deal with these. To achieve this purpose, our system is equipped with deep learning techniques to detect the user’s negative facial expressions, which in turn can be used as cues for the robot to proactively induce self-disclosure. Once triggered, using a conversational model, the robot engages the user to determine the cause of their negative mood. Then, it infers the user’s internal feelings by applying Markov Chain Monte Carlo (MCMC) inference over a Bayesian Network on the user’s utterance. Combining the information gathered from the concept inferencing process and the self-disclosure content, the system is able to estimate a set of desires from the Bayesian Network. Experiments show that our proposed work can correctly infer the user’s feelings and desires from their utterances, as well as generate an appropriate response, resulting in the improvement of human’s trust toward the robot.