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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭士康 | |
| dc.contributor.author | Shao-Yu Chang | en |
| dc.contributor.author | 張邵瑀 | zh_TW |
| dc.date.accessioned | 2021-05-11T04:57:58Z | - |
| dc.date.available | 2019-08-13 | |
| dc.date.available | 2021-05-11T04:57:58Z | - |
| dc.date.copyright | 2019-08-13 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-08 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/686 | - |
| dc.description.abstract | 在本論文中，我們提出了一種新穎的視覺和運動協調系統機器人作為長期護理長者的物理代理人操作鍵盤。要年長者去學習現代多樣化的應用程式是如何使用是一項非常困難的挑戰，如果機器人可以為他們操作這些智慧裝置，勢必可以大幅減少照護者的負擔。我們所提出的系統使用卷積神經網絡物件偵測來感知目標按鈕位置，並通過深度神經網絡來控制其動作。我們設計了一個虛擬代理人NAOgym，他負責管理機器人感知和運動模型之間的訊息交換。我們使用了基於CNN的視覺模型來偵測顯示在平板電腦上的目標按鍵與出現在視線中的觸控筆，並且計算它們的相對位置和距離作為觀察到的高階語義信息。而基於DNN的運動模型，將會根據結合了相對位置與物理代理人傳感器的狀態訊息，通過運動模型的策略來產生下一個動作。另外，我們把注意機制應用在動作控制模型上，並將其受專注的程度當作關節的運動速度，來加速強化學習演算法的訓練。在虛擬手臂環境中，我們設計了像NAO一樣的手臂來評估訓練過程和效能，特徵的選擇對效能的影響以及演算法對無須預先校准的假設。通過虛擬手臂進行實驗以評估所提出的系統。實驗結果驗證了我們提出的概念。 | zh_TW |
| dc.description.abstract | In this work, we propose a novel vision and motion coordination system robot as a physical agent typing keyboard for elders in long-term care. It is a challenge for older people to learn how to use modern and diverse applications; if robots can operate these smart devices for them, it will inevitably reduce the burden on caregivers. Our proposed system uses convolutional neural network object detection to sense the position of the target button and control its motion through a deep neural network. We designed a cyber-agent, NAOgym, who manages the exchange of information between robot perception and motion models. We used a CNN-based model to detect the target buttons displayed on the tablet computer and the stylus pen that appeared in sight, and calculate their relative position and distance as the observed high-level semantic information. The DNN-based actor model will generate the next action through the policy of the actor model based on the state information combined with the relative position and the physical agent sensor. In addition, we apply the attention mechanism to the motion control model and use the degree of concentration as the speed of the joint to accelerate the training of the reinforcement learning algorithm. In the virtual arm environment, we design an arm like NAO’s to evaluate the training process and performance, the features affection to the performance, and the calibration-free assumption of the algorithm. The experiments are conducted through the virtual arm environment to evaluate the proposed system. Experiment results verify the conception we proposed. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-11T04:57:58Z (GMT). No. of bitstreams: 1 ntu-108-R06921081-1.pdf: 4002871 bytes, checksum: d6cecd6fa6809349340153c3a65e2b12 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Objective and Motivation 1 1.2 Problem Statement 3 1.3 Literature Survey and Related Work 3 1.4 Contributions 7 1.5 Chapter Outline 7 Chapter 2 Background 8 2.1 Physical Agent: NAO 8 2.1.1 Introduction 8 2.1.2 Features 8 2.1.3 Broker 9 2.1.4 Programming Language 10 2.2 Computer Vision 10 2.2.1 Optical Character Recognition 10 2.2.2 YOLO 11 2.3 Robotic Motion Control 13 2.4 Virtual Environment 16 Chapter 3 System Design 18 3.1 System Scheme 18 3.2 NAO core 19 3.3 Vision Model 20 3.3.1 Detection 21 3.3.2 Training Vision model on Our Task 22 Figure 3.4 Detecting pen tail and target button 22 3.4 Action Generator 23 3.4.1 Training Core 23 3.5 Virtual Arm Environment 23 Figure 3.6 Virtual arm environment 24 3.6 Attention DDPG 24 Chapter 4 Experiment Design and Implementation 26 4.1 Experiment Platform 26 4.1.1 NAO 26 4.1.2 Physical Setup 27 4.1.3 Remote Computation System Setup 29 4.1.4 Virtual Keyboard on the Tablet 30 4.1.5 NAO gym package 31 4.2 Vision 32 4.2.1 Transfer Learning 32 4.2.2 Data Augmentation 33 4.3 Motion 37 4.3.1 Constraints on Joint Angles 37 4.3.2 Reward Engineering 38 4.3.3 State Observation 39 4.3.4 Attention Mechanism 40 Chapter 5 Experiment Results and Discussion 42 5.1 Performance of Attention-OCR and YOLO 42 5.2 Attention-Based Actor 44 5.2.1 Performance in Virtual Arm v0 46 5.2.2 Performance with Absolute Position 48 5.2.1 Performance with Randomized Arm 48 5.3 Type Single Character 50 Chapter 6 Conclusion 51 References 52 Appendix 55 | |
| dc.language.iso | en | |
| dc.subject | NAO | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 強化學習 | zh_TW |
| dc.subject | 機器人動作控制 | zh_TW |
| dc.subject | NAO | en |
| dc.subject | reinforcement learning | en |
| dc.subject | continuous robotic control | en |
| dc.subject | convolution neural network | en |
| dc.title | 基於深度學習及遷移式學習之機器人操作平板電腦虛擬鍵盤的視覺與動作協調系統 | zh_TW |
| dc.title | A Vision and Motion Coordination System Based on Deep Learning and Transfer Learning for a Robot to Type Virtual Keyboards on a Tablet Computer | en |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李弘毅,袁世一 | |
| dc.subject.keyword | 機器學習,強化學習,機器人動作控制,NAO, | zh_TW |
| dc.subject.keyword | reinforcement learning,continuous robotic control,convolution neural network,NAO, | en |
| dc.relation.page | 56 | |
| dc.identifier.doi | 10.6342/NTU201902835 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2019-08-08 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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|---|---|---|---|
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