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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 徐宏民 | zh_TW |
| dc.contributor.advisor | Winston H. Hsu | en |
| dc.contributor.author | 陳靖元 | zh_TW |
| dc.contributor.author | Ching-Yuan Chen | en |
| dc.date.accessioned | 2024-08-15T16:49:01Z | - |
| dc.date.available | 2024-08-16 | - |
| dc.date.copyright | 2024-08-15 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-01 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94323 | - |
| dc.description.abstract | 在無需訓練的機器人應用中，以視覺語言模型（VLMs）探索構建的預探索語義地圖作為基礎元素已被證明非常有效。然而，現有的方法假設地圖是準確的，並且沒有提供有效的機制來根據錯誤的地圖修正決策。這篇論文引入了不確定概念，並通過不確定性來估計地圖的準確性和品質，使得機器人可以找到更有可能出錯的區域，並在沒有額外標籤的情況下修正由不準確地圖引起的錯誤決策。
我們使用兩個現代地圖的基石模型，VLMaps 和 OpenMask3D，展示了我們所提出方法的有效性，並在這兩者上都顯示了改善。 | zh_TW |
| dc.description.abstract | Pre-Explored Semantic Map, constructed through prior exploration using visual lan guage models (VLMs), has proven effective as a foundational element for training-free robotic applications. However, existing approaches assume the maps accuracy and do not provide effective mechanisms for revising decisions based on incorrect maps. This work introduces Uncertainty-Aware Memory Updating and Re-Proposing, which estimates the accuracy and quality of the map through uncertainty, enabling the agent to locate the areas with higher chance of being incorrect and to revise erroneous decisions stemming from inaccurate maps without additional labels.
We demonstrate the effectiveness of our proposed method using two modern map backbones, VLMaps and OpenMask3D, and show the improvements on both of them. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-15T16:49:01Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-15T16:49:01Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xi List of Tables xiii Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Pre-Explored Semantic Map . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Perception with Visual Language Model . . . . . . . . . . . . . . . . 6 2.3 Active Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 3 Method 9 3.1 Failure Case Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Framework Overview . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3.1 Efficient Map Update . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3.2 Re-Proposing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.4 Uncertainty Measures . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.4.1 Single-view: Entropy . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.4.2 Multi-view: Standard Error & KL-Divergence . . . . . . . . . . . . 15 Chapter 4 Experiments 17 4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.3.1 Efficacy of Each Module . . . . . . . . . . . . . . . . . . . . . . . 20 4.3.2 Efficient Map Update . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3.2.1 Selection of Uncertainty Measures . . . . . . . . . . . 21 4.3.2.2 Generalizability . . . . . . . . . . . . . . . . . . . . . 22 4.3.3 Re-proposing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3.3.1 Selection of Confidence and Uncertainty Measures . . 22 4.3.3.2 Generalizability . . . . . . . . . . . . . . . . . . . . . 23 Chapter 5 Conclusion 25 References 27 Appendix A — More Method Details 31 A.1 Pseudocode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 A.2 Limitation of KL Divergence . . . . . . . . . . . . . . . . . . . . . . 32 | - |
| dc.language.iso | en | - |
| dc.subject | 預探索 | zh_TW |
| dc.subject | 不確定性 | zh_TW |
| dc.subject | 導航 | zh_TW |
| dc.subject | Embodied Agent Navigation | en |
| dc.subject | Uncertainty | en |
| dc.subject | Pre-Explore | en |
| dc.title | 基於不確定性之地圖更新與再查找 | zh_TW |
| dc.title | Uncertainty-Aware Memory Updating and Re-Proposing | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳尚澤;陳文進;葉梅珍 | zh_TW |
| dc.contributor.oralexamcommittee | Shang-Tse Chen;WC Chen;Mei-Chen Yeh | en |
| dc.subject.keyword | 不確定性,預探索,導航, | zh_TW |
| dc.subject.keyword | Uncertainty,Pre-Explore,Embodied Agent Navigation, | en |
| dc.relation.page | 32 | - |
| dc.identifier.doi | 10.6342/NTU202402241 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-04 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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