Enhanced behavior system of service robots based on BERT, CNN, and DQN
Y. Zhang, Y. Sung
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AbstractIn the past 20 years, the research in the field of robotics and intelligent systems has gradually increased. With the advancement of related fields, service robots have received extensive attention from industry and academia (R. Reddy, 2006). The increasingly serious aging trend has brought great challenges to the current society. Due to illness or physical decline, it is difficult for the elderly to carry out their daily life smoothly. Service robots can actively interact with the elderly and provide corresponding help. For example, reminding meals, reminding taking medicines, assisting in doing sports and so on are possible (Mast M et al., 2015). For the safety assurance of the elderly, the robot should be actuated properly for speed regulation and strong recognition ability, combined with an audiovisual system to prevent accidental collision with elderly people. In elderly care, robots need to respond to the voice commands of the elderly, and current speech recognition technology makes this possible. (Park C et al., 2012). This paper proposes a well-established software engineering approach for properly coordinating service robotic systems.Keywords: deep reinforcement learning, service robot, behavior system, elderly living, human-robot interaction
Y. Zhang, Y. Sung (2022). Enhanced behavior system of service robots based on BERT, CNN, and DQN. Gerontechnology, 21(s),1-1
https://doi.org/10.4017/gt.2022.21.s.533.pp5