Active Learning on Service Providing Model: Adjustment of Robot Behaviors Through Human Feedback

As robots are put into humans' daily life, the assigned tasks to robots are varied, and the different needs of people interacting with robots are immense. As a result, when facing different users, it is important for robots to personalize the interactions and provide user-desired services. This paper, therefore, proposes a learning strategy on the service-providing model. Through human feedback, the strategy enables the robot to learn the users' needs, as well as preferences, and adjust its behaviors. Here, we assume that users' needs and preferences may vary with time; hence the goal of this paper is to let the adjustment of robot behaviors be able to adapt to those variations. In turn, the service-providing model of the robot could adjust online as well. That is, it can select a new action from those favorable actions that have already been selected or an action that is not an unfavorable action but has annoyed humans recently. To implement our system, the service robot under discussion is applied to the home environment. For performance evaluation, we have performed extensive experiments that satisfactorily demonstrate that our robot can provide services to different users and adapt to their preference change.