로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제16권1호
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  • Pages.56-63
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  • 2021
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  • 1975-6291(pISSN)
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  • 2287-3961(eISSN)
한국로봇학회 (Korea Robotics Society)
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정리정돈을 위한 Q-learning 기반의 작업계획기

Tidy-up Task Planner based on Q-learning

  • 투고 : 2020.11.03
  • 심사 : 2020.12.17
  • 발행 : 2021.02.26
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초록

As the use of robots in service area increases, research has been conducted to replace human tasks in daily life with robots. Among them, this study focuses on the tidy-up task on a desk using a robot arm. The order in which tidy-up motions are carried out has a great impact on the success rate of the task. Therefore, in this study, a neural network-based method for determining the priority of the tidy-up motions from the input image is proposed. Reinforcement learning, which shows good performance in the sequential decision-making process, is used to train such a task planner. The training process is conducted in a virtual tidy-up environment that is configured the same as the actual tidy-up environment. To transfer the learning results in the virtual environment to the actual environment, the input image is preprocessed into a segmented image. In addition, the use of a neural network that excludes unnecessary tidy-up motions from the priority during the tidy-up operation increases the success rate of the task planner. Experiments were conducted in the real world to verify the proposed task planning method.

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참고문헌

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