정리정돈을 위한 Q-learning 기반의 작업계획기Tidy-up Task Planner based on Q-learning
- Other Titles
- Tidy-up Task Planner based on Q-learning
- Authors
- 양민규; 안국현; 송재복
- Issue Date
- 2021
- Publisher
- 한국로봇학회
- Keywords
- Deep Learning; Object Detection; Q-learning; Reinforcement Learning; Robot Learning
- Citation
- 로봇학회 논문지, v.16, no.1, pp.056 - 063
- Indexed
- KCI
- Journal Title
- 로봇학회 논문지
- Volume
- 16
- Number
- 1
- Start Page
- 056
- End Page
- 063
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/137977
- ISSN
- 1975-6291
- Abstract
- 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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Collections - College of Engineering > Department of Mechanical Engineering > 1. Journal Articles
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