Human-Robot full-sentence VQA interaction system with highway memory network = Highway memory network를 이용한 완전한 문장의 인간-로봇 시각 질의응답 상호작용 시스템

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One of the major functions of intelligent robots such as social or home service robots is to interact with users in natural language. Moving on from simple conversation or retrieval of data stored in computer memory, we present a new Human-Robot Interaction (HRI) system which can understand and reason over environment around the user and provide information about it in a natural language. For its intelligent interaction, we developed a deep learning network model based on Dynamic Memory Networks (DMN), a deep learning network for Visual Question Answering (VQA), and proposed Highway Memory Network (HMN) and Full-Sentence Highway Memory Network (FSHMN). For its hardware, we built a robotic head platform with a tablet PC and a 3 DOF neck. Through an experiment where the user and the robot had question answering interaction in our customized environment and in real time, the feasibility our proposed system was validated, and the e ectiveness of deep learning application in real world as well as a new insight on human robot interaction was demonstrated.
Advisors
Kim, Jong-Hwanresearcher김종환researcher
Description
한국과학기술원 :로봇공학학제전공,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 로봇공학학제전공, 2018.2,[iv, 32 p. :]

Keywords

Human-Robot Interaction (HRI)▼aVisual Question Answering; 인간로봇상호작용▼a시각질의응답▼a로봇▼a심층학습▼a자연어처리

URI
http://hdl.handle.net/10203/266001
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733780&flag=dissertation
Appears in Collection
RE-Theses_Master(석사논문)
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