The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Deep Reinforcement Learning-Based Cooperative Robot Using Facial Feedback

표정 피드백을 이용한 딥강화학습 기반 협력로봇 개발

  • Jeon, Haein (Department of Artificial Intelligence, Kyungpook National University) ;
  • Kang, Jeonghun (Department of Artificial Intelligence, Kyungpook National University) ;
  • Kang, Bo-Yeong (Department of Robot and Smart System Engineering, Kyungpook National University)
  • Received : 2022.05.27
  • Accepted : 2022.07.06
  • Published : 2022.08.31
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Abstract

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

Keywords

Acknowledgement

This research was supported by Kyungpook National University Research Fund, 2021

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