정리정돈을 위한 Q-learning 기반의 작업계획기
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 양민규 | - |
dc.contributor.author | 안국현 | - |
dc.contributor.author | 송재복 | - |
dc.date.accessioned | 2022-03-06T11:40:21Z | - |
dc.date.available | 2022-03-06T11:40:21Z | - |
dc.date.created | 2022-02-10 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1975-6291 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137977 | - |
dc.description.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. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국로봇학회 | - |
dc.title | 정리정돈을 위한 Q-learning 기반의 작업계획기 | - |
dc.title.alternative | Tidy-up Task Planner based on Q-learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 송재복 | - |
dc.identifier.bibliographicCitation | 로봇학회 논문지, v.16, no.1, pp.056 - 063 | - |
dc.relation.isPartOf | 로봇학회 논문지 | - |
dc.citation.title | 로봇학회 논문지 | - |
dc.citation.volume | 16 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 056 | - |
dc.citation.endPage | 063 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002684041 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Object Detection | - |
dc.subject.keywordAuthor | Q-learning | - |
dc.subject.keywordAuthor | Reinforcement Learning | - |
dc.subject.keywordAuthor | Robot Learning | - |
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