Generating Adaptive Attending Behaviors using User State Classification and Deep Reinforcement Learning

Yoshiki Kohari; Jun Miura; Shuji Oishi

IROS 2018poster8 citations

Generating Adaptive Attending Behaviors using User State Classification and Deep Reinforcement Learning

Abstract

This paper describes a method of generating attending behaviors adaptively to the user state. The method classifies the user state based on user information such as the relative position and the orientation. For each classified state, the method executes the corresponding policy for behavior generation, which has been trained using a deep reinforcement learning, namely DDPG (deep deterministic policy gradient). We use as a state space of DDPG a distance-transformed local map with person information, and define reward functions suitable for respective user states. We conducted attending experiments both in a simulated and a real environment to show the effectiveness of the proposed method.

BibTeX

@inproceedings{iros2018_generatingadapti,
  title = {Generating Adaptive Attending Behaviors using User State Classification and Deep Reinforcement Learning},
  author = {Yoshiki Kohari and Jun Miura and Shuji Oishi},
  booktitle = {IROS 2018},
  year = {2018}
}