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標題: | 社交型協作機器人基於情境的涵意提供適切的服務 Social Co-Robot for Just-Good Services Based on Situational Context Perception |
作者: | Chung-Kai Hsieh 謝仲凱 |
指導教授: | 羅仁權(Ren C. Luo) |
關鍵字: | 人機互動,深度學習,情境感知, Human-Robot Interaction,Deep Learning,Situational Context Perception, |
出版年 : | 2017 |
學位: | 碩士 |
摘要: | 本文的目的是提出一種社交型協作機器人應用情境的涵意,學習並預測人類 的想法,進而提供「剛剛好的服務」。為了在人類社交環境中與人友善的互動, 機器人應具有情境感知瞭解人類社交技巧的能力並且表現出得體的行為。
在本文中,情境式上下文著重在讓機器人感知他人是否需要幫助,根據預測 的人類想法,機器人提供剛剛好的服務。剛剛好的概念來自鼎泰豐餐廳的董事長, 他說:「服務不足,是怠慢;殷勤過頭,變成打擾,『剛剛好的服務』是鼎泰豐 團隊努力追求的目標」。在服務業方面,當顧客需要幫助時,服務員主動提供服 務是非常暖心的。換句話說,當顧客不需要幫助時,不去打擾他們是很體貼的。 我們提出兩個深度學習模型,作為機器人的情境式上下文感知,並從人機互 動中觀察並學習判斷人類的意圖。基於深度學習模型,我們賦予機器人感知人的 意向的能力。因此,機器人可以基於預測的人類心理狀態,做出適當的社交行為。 實驗結果表明,與常規分類器相比,我們提出的深度學習模型可以使機器人顯著 提高預測人類思維的準確性。此外,在判斷人是否需要幫忙的任務上,基於情境 式上下文的預測結果與服務業人士的意見保持高度一致。 The objective of this thesis is to develop a social co-robot for provision of “just-good services” using situational context perception for learning and predicting human’s mentation. To interact with humans in Human Social Environments (HSEs), robots are expected to possess the ability of situational context perception and behave appropriately. In this paper, we employ the concept of situational context to our work, which mainly focus on making robots perceive others’ needing assistance and provide “just- good service”. The just-good concept is stem from the owner of Din Tai Fung restaurant, and he says: Inadequate service is neglecting; too diligent become disturbing, just-good service is the goal Ding Tai Fung team pursue.” In service industry, it is indeed friendly to help others as they need. In other words, it is actually considerate not to bother others when they don’t need help. We propose two deep learning models, as situational context perception of robot, to learn from observations of human-robot interaction. Based on these models, we endow robot the capability of perceiving human’s mentation. Thus, the appropriate social behaviors can be performed by the robot with respect to human’s mental state. The experimental results demonstrate that robot can significantly improve the accuracy of predicting a person’s mentation through the proposed deep learning models by comparison with conventional classifiers. Furthermore, the prediction of our situational context perception keep highly consistent with the opinion made by people who work in service industry. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2301 |
DOI: | 10.6342/NTU201703477 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 電機工程學系 |
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ntu-106-1.pdf | 28.56 MB | Adobe PDF | 檢視/開啟 |
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