Reactive virtual agent learning for NUI-based HRI applications

The natural user interface (NUI) has been investigated in a variety of fields in application software. This paper proposes an approach to generate virtual agents that can support users for NUI-based applications through human–robot interaction (HRI) learning in a virtual environment. Conventional human–robot interaction (HRI) learning is carried out by repeating processes that are time-consuming, complicated and dangerous because of certain features of robots. Therefore, a method is needed to train virtual agents that interact with virtual humans imitating human movements in a virtual environment. Then the result of this virtual agent can be applied to NUI-based interactive applications after the interaction learning is completed. The proposed method was applied to a model of a typical house in virtual environment with virtual human performing daily-life activities such as washing, eating, and watching TV. The results show that the virtual agent can predict a human’s intent, identify actions that are helpful to the human, and can provide services 16 % faster than a virtual agent trained using traditional Q-learning.