Hajime Asama 研究室
主宰者：Hajime Asama
東京大学
AI 要約（直近 5 年の研究成果）
Hajime Asama研究室は、ロボットを用いた産業インフラの検査・維持管理と自動化作業の実現に取り組んでいます。石油精製施設や原発といった複雑で危険な環境で活動するロボットが、映像や音声データから異常を検出する必要があります。例えば、過去と現在の検査映像を比較して設備の異変を識別したり、背景雑音の中から故障音を見つけ出したりする課題に対して、深層学習や信号処理を組み合わせたシステムの開発を進めています。
同時に、建設現場や未知の粗い地形での自動作業に必要な知覚・制御技術の研究も展開しています。建設現場の多様な物体を認識するため複数の視覚言語モデルを統合する手法、土質を推定して最適な掘削経路を生成する方法、複数ロボットが協調して不規則な地形を移動する機構など、様々な環境適応型のシステムを提案しています。これらの研究では、3次元点群データやシミュレータを活用した最適化、強化学習による行動計画などの手法が採用されています。
さらに、原発廃炉作業や放射線環境での遠隔操作支援システムの開発にも従事しており、ロボット工学とAI技術を統合して、人間が立ち入れない現場での信頼性の高い自動化と遠隔操作環境の実現を目指しています。
※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。
外部リンク
関連研究室(8 件)
- 農学・生物科学Takanori Fukao 研究室東京大学論文 31 件·共通: ロボット, 工学, 機械・ロボティクス, ロボティクス +13
- エネルギーKenichi Furuhashi 研究室東京大学論文 21 件·共通: ロボット, 工学, 機械・ロボティクス, ロボティクス +13
- 神経科学Katsumi Watanabe 研究室早稲田大学論文 25 件·共通: 学習, 生物学, 神経科学, 認知・行動 +8
- 農学・生物科学Kazuhiro Fujiwara 研究室東京大学論文 21 件·共通: 生物学, システム, 情報工学, 計算機科学 +8
- 神経科学Ryohei Kanzaki 研究室東京大学論文 33 件·共通: 行動, 生物学, 神経科学, 認知・行動 +7
- 農学・生物科学Ko Mochizuki 研究室東京大学論文 22 件·共通: 生物学, 化学, 行動, 神経科学 +7
- 医学Yutaka Suzuki 研究室東京大学論文 100 件·共通: 生物学, 制御, 工学, 機械・ロボティクス +6
- 農学・生物科学Wei Guo 研究室東京大学論文 77 件·共通: 学習, 生物学, 神経科学, 認知・行動 +6
研究成果（183 件）
- DOI: https://doi.org/10.1109/sii64115.2026.11404505
- [2026] Spectral-to-Spatial Distillation: Denoising Framework for Real-Time Anomalous Sound DetectionDOI: https://doi.org/10.1109/sii64115.2026.11404617
- DOI: https://doi.org/10.20965/jrm.2026.p0672
- DOI: https://doi.org/10.20965/jrm.2026.p0672
- [2026] EVLOD: Ensemble Vision-Language Open-vocabulary Detection for Construction Site Object RecognitionDOI: https://doi.org/10.1109/sii64115.2026.11404560
- [2026] EVLOD: Ensemble Vision-Language Open-vocabulary Detection for Construction Site Object RecognitionDOI: https://doi.org/10.1109/sii64115.2026.11404560
- DOI: https://doi.org/10.1109/sii64115.2026.11404505
- [2026] Spectral-to-Spatial Distillation: Denoising Framework for Real-Time Anomalous Sound DetectionDOI: https://doi.org/10.1109/sii64115.2026.11404617
- DOI: https://doi.org/10.1109/tro.2025.3562048
- [2025] Change Detection with a Mobile Robot Using Reference Region Extraction Based on Multi-View StereoDOI: https://doi.org/10.1541/ieejeiss.145.775
続きを表示（残り 173 件）閉じる
- DOI: https://doi.org/10.20965/ijat.2025.p0554
- DOI: https://doi.org/10.20965/ijat.2025.p0178
- DOI: https://doi.org/10.20965/ijat.2025.p0192
- DOI: https://doi.org/10.2468/jbes.76.86
- DOI: https://doi.org/10.1186/s40648-025-00295-5
- DOI: https://doi.org/10.1109/lra.2025.3549663
- DOI: https://doi.org/10.1109/sii59315.2025.10870888
- DOI: https://doi.org/10.1109/tro.2025.3562048
- DOI: https://doi.org/10.3327/taesj.j24.012
- DOI: https://doi.org/10.1109/iros60139.2025.11245904
- DOI: https://doi.org/10.1109/iros60139.2025.11245904
- [2025] Change Detection with a Mobile Robot Using Reference Region Extraction Based on Multi-View StereoDOI: https://doi.org/10.1541/ieejeiss.145.775
- DOI: https://doi.org/10.20965/ijat.2025.p0554
- DOI: https://doi.org/10.20965/ijat.2025.p0178
- DOI: https://doi.org/10.20965/ijat.2025.p0192
- DOI: https://doi.org/10.1080/01691864.2024.2376029
- DOI: https://doi.org/10.1109/lra.2024.3421792
- DOI: https://doi.org/10.1109/iros58592.2024.10802571
- DOI: https://doi.org/10.1080/01691864.2024.2376029
- DOI: https://doi.org/10.1109/lra.2024.3421792
- DOI: https://doi.org/10.1080/01691864.2024.2358424
- DOI: https://doi.org/10.20965/jrm.2024.p0079
- DOI: https://doi.org/10.1080/01691864.2024.2358424
- DOI: https://doi.org/10.20965/jrm.2024.p0375
- DOI: https://doi.org/10.20965/jrm.2024.p0079
- DOI: https://doi.org/10.1109/sii58957.2024.10417462
- [2024] Change Detection in Pipe Image Pairs Extracted from Inspection Videos by Sequential FilteringDOI: https://doi.org/10.1109/sii58957.2024.10417231
- DOI: https://doi.org/10.1109/sii58957.2024.10417208
- DOI: https://doi.org/10.1109/sii58957.2024.10417590
- DOI: https://doi.org/10.1109/sii58957.2024.10417712
- DOI: https://doi.org/10.1109/sii58957.2024.10417462
- DOI: https://doi.org/10.1109/sii58957.2024.10417208
- DOI: https://doi.org/10.1109/sii58957.2024.10417590
- DOI: https://doi.org/10.1109/sii58957.2024.10417712
- DOI: https://doi.org/10.1109/sii58957.2024.10417582
- DOI: https://doi.org/10.1109/lra.2024.3349968
- DOI: https://doi.org/10.1109/lra.2024.3349968
- [2024] Blend AutoAugment: Automatic Data Augmentation for Image Classification Using Linear BlendingDOI: https://doi.org/10.1109/access.2024.3401167
- DOI: https://doi.org/10.9746/sicetr.60.27
- DOI: https://doi.org/10.1299/jsmermd.2024.2p1-b03
- [2024] Estimation of Rock Parameters for Automatic Excavation of Subsurface Rocks by Hydraulic ExcavatorDOI: https://doi.org/10.1299/jsmermd.2024.2p1-b04
- DOI: https://doi.org/10.1299/jsmermd.2024.2a1-k06
- [2024] Blend AutoAugment: Automatic Data Augmentation for Image Classification Using Linear BlendingDOI: https://doi.org/10.1109/access.2024.3401167
- DOI: https://doi.org/10.9746/sicetr.60.27
- DOI: https://doi.org/10.1299/jsmermd.2024.2p1-b03
- [2024] Estimation of Rock Parameters for Automatic Excavation of Subsurface Rocks by Hydraulic ExcavatorDOI: https://doi.org/10.1299/jsmermd.2024.2p1-b04
- DOI: https://doi.org/10.1299/jsmermd.2024.2a1-k06
- DOI: https://doi.org/10.1299/jsmermd.2024.2a1-l01
- DOI: https://doi.org/10.1299/jsmefdr.2024.0_1011
- DOI: https://doi.org/10.1299/jsmermd.2024.2a1-l01
- DOI: https://doi.org/10.1299/jsmermd.2024.1p2-m01
- DOI: https://doi.org/10.1299/jsmefdr.2024.0_1011
- DOI: https://doi.org/10.1299/jsmedsd.2024.34.1406
- DOI: https://doi.org/10.1299/jsmermd.2024.1p2-m01
- DOI: https://doi.org/10.1109/sii58957.2024.10417582
- DOI: https://doi.org/10.1109/ssrr62954.2024.10770049
- DOI: https://doi.org/10.23919/iccas63016.2024.10773044
- DOI: https://doi.org/10.20965/jrm.2024.p0813
- DOI: https://doi.org/10.1109/ssrr62954.2024.10770049
- DOI: https://doi.org/10.23919/iccas63016.2024.10773044
- DOI: https://doi.org/10.1109/iros58592.2024.10802571
- DOI: https://doi.org/10.20965/jrm.2024.p0813
- DOI: https://doi.org/10.1080/01691864.2024.2388119
- DOI: https://doi.org/10.1109/sii55687.2023.10039168
- DOI: https://doi.org/10.2493/jjspe.89.190
- DOI: https://doi.org/10.1299/jsmermd.2023.1a2-g02
- DOI: https://doi.org/10.1299/jsmermd.2023.1p1-a23
- DOI: https://doi.org/10.1299/jsmermd.2023.1p1-a23
- [2023] Negotiation Flow for Multi-Team Collaborative Sediment Transportation in an Unlimited EnvironmentDOI: https://doi.org/10.1299/jsmermd.2023.1p1-g02
- [2023] Negotiation Flow for Multi-Team Collaborative Sediment Transportation in an Unlimited EnvironmentDOI: https://doi.org/10.1299/jsmermd.2023.1p1-g02
- DOI: https://doi.org/10.1299/jsmermd.2023.2p2-a24
- DOI: https://doi.org/10.1299/jsmermd.2023.2p2-a24
- DOI: https://doi.org/10.1299/jsmermd.2023.1p1-b01
- DOI: https://doi.org/10.1299/jsmermd.2023.2a1-a19
- DOI: https://doi.org/10.1299/jsmermd.2023.2a1-a19
- DOI: https://doi.org/10.1109/iros55552.2023.10341601
- DOI: https://doi.org/10.1109/iros55552.2023.10341477
- [2023] Risk-Sensitive Mobile Robot Navigation in Crowded Environment via Offline Reinforcement LearningDOI: https://doi.org/10.1109/iros55552.2023.10341948
- DOI: https://doi.org/10.1109/iros55552.2023.10341601
- DOI: https://doi.org/10.1109/iros55552.2023.10341477
- [2023] Risk-Sensitive Mobile Robot Navigation in Crowded Environment via Offline Reinforcement LearningDOI: https://doi.org/10.1109/iros55552.2023.10341948
- DOI: https://doi.org/10.1002/rob.22241
- DOI: https://doi.org/10.1002/rob.22241
- DOI: https://doi.org/10.3389/fnins.2023.1094658
- DOI: https://doi.org/10.1109/aim46323.2023.10196157
- DOI: https://doi.org/10.1109/ur57808.2023.10202279
- DOI: https://doi.org/10.1109/ur57808.2023.10202270
- DOI: https://doi.org/10.2493/jjspe.89.328
- DOI: https://doi.org/10.3389/fnins.2023.1094658
- DOI: https://doi.org/10.1109/aim46323.2023.10196157
- DOI: https://doi.org/10.1109/ur57808.2023.10202279
- DOI: https://doi.org/10.1109/ur57808.2023.10202270
- DOI: https://doi.org/10.2493/jjspe.89.328
- DOI: https://doi.org/10.2493/jjspe.89.190
- DOI: https://doi.org/10.1109/lsens.2023.3240745
- DOI: https://doi.org/10.1109/sii55687.2023.10039098
- DOI: https://doi.org/10.1109/lsens.2023.3240745
- DOI: https://doi.org/10.1109/sii55687.2023.10039098
- DOI: https://doi.org/10.1109/sii55687.2023.10039267
- DOI: https://doi.org/10.1109/sii55687.2023.10039303
- DOI: https://doi.org/10.1109/sii55687.2023.10039168
- DOI: https://doi.org/10.1109/sii55687.2023.10039267
- DOI: https://doi.org/10.1109/sii55687.2023.10039358
- DOI: https://doi.org/10.1109/sii55687.2023.10039303
- DOI: https://doi.org/10.2493/jjspe.89.105
- DOI: https://doi.org/10.2493/jjspe.89.105
- [2023] Applying Albedo Estimation and Implicit Neural Representations to Well-Posed Shape From ShadingDOI: https://doi.org/10.1109/access.2023.3269286
- DOI: https://doi.org/10.1016/j.ifacol.2023.10.435
- DOI: https://doi.org/10.1016/j.ifacol.2023.10.549
- DOI: https://doi.org/10.1016/j.ifacol.2023.10.705
- DOI: https://doi.org/10.1299/jsmermd.2023.1a2-g03
- [2023] Viewpoint Selection for the Efficient Teleoperation of a Robot Arm Using Reinforcement LearningDOI: https://doi.org/10.1109/access.2023.3327826
- [2023] Applying Albedo Estimation and Implicit Neural Representations to Well-Posed Shape From ShadingDOI: https://doi.org/10.1109/access.2023.3269286
- DOI: https://doi.org/10.1016/j.ifacol.2023.10.435
- DOI: https://doi.org/10.1016/j.ifacol.2023.10.549
- DOI: https://doi.org/10.1016/j.ifacol.2023.10.705
- DOI: https://doi.org/10.1299/jsmermd.2023.1a2-g03
- DOI: https://doi.org/10.1038/s41598-021-04437-8
- DOI: https://doi.org/10.1109/sii52469.2022.9708756
- DOI: https://doi.org/10.1109/sii52469.2022.9708811
- DOI: https://doi.org/10.2493/jjspe.88.162
- DOI: https://doi.org/10.1109/sii52469.2022.9708607
- DOI: https://doi.org/10.1109/sii52469.2022.9708756
- DOI: https://doi.org/10.1109/sii52469.2022.9708809
- DOI: https://doi.org/10.1109/sii52469.2022.9708811
- [2022] Radiation distribution estimation with a non-directional detector using a plane source model*DOI: https://doi.org/10.1080/01691864.2021.2016482
- DOI: https://doi.org/10.1063/5.0095596
- DOI: https://doi.org/10.7210/jrsj.40.589
- DOI: https://doi.org/10.1299/jsmermd.2022.1p1-b09
- DOI: https://doi.org/10.1016/j.heliyon.2022.e12117
- [2022] Robot navigation in crowds via deep reinforcement learning with modeling of obstacle uni-actionDOI: https://doi.org/10.1080/01691864.2022.2142068
- DOI: https://doi.org/10.1109/iros47612.2022.9982049
- DOI: https://doi.org/10.20965/jrm.2022.p0767
- DOI: https://doi.org/10.1137/21m1414279
- DOI: https://doi.org/10.1016/j.heliyon.2022.e12117
- DOI: https://doi.org/10.1109/iros47612.2022.9982049
- DOI: https://doi.org/10.20965/jrm.2022.p0767
- DOI: https://doi.org/10.3390/s22124325
- DOI: https://doi.org/10.2493/jjspe.88.919
- DOI: https://doi.org/10.2493/jjspe.88.919
- DOI: https://doi.org/10.1137/21m1414279
- DOI: https://doi.org/10.3390/s22124325
- [2022] Radiation distribution estimation with a non-directional detector using a plane source model*DOI: https://doi.org/10.1080/01691864.2021.2016482
- DOI: https://doi.org/10.7210/jrsj.40.589
- DOI: https://doi.org/10.3389/fnsys.2022.785143
- DOI: https://doi.org/10.1109/iciprob54042.2022.9798718
- DOI: https://doi.org/10.1080/01691864.2022.2043184
- DOI: https://doi.org/10.2493/jjspe.88.282
- DOI: https://doi.org/10.1109/oceanschennai45887.2022.9775502
- DOI: https://doi.org/10.1299/jsmermd.2022.1p1-b09
- DOI: https://doi.org/10.3389/fnsys.2022.785143
- DOI: https://doi.org/10.1109/iciprob54042.2022.9798718
- DOI: https://doi.org/10.1080/01691864.2022.2043184
- DOI: https://doi.org/10.2493/jjspe.88.282
- DOI: https://doi.org/10.1109/oceanschennai45887.2022.9775502
- DOI: https://doi.org/10.1109/sii52469.2022.9708815
- DOI: https://doi.org/10.2493/jjspe.88.162
- DOI: https://doi.org/10.1038/s41598-021-04437-8
- DOI: https://doi.org/10.1109/sii52469.2022.9708815
- DOI: https://doi.org/10.1109/sii52469.2022.9708607
- DOI: https://doi.org/10.3390/s21216975
- [2021] Scale Optimization of Structure from Motion for Structured Light-based All-round 3D MeasurementDOI: https://doi.org/10.1109/smc52423.2021.9658793
- DOI: https://doi.org/10.1038/s41598-021-99969-4
- DOI: https://doi.org/10.1080/01691864.2021.2007167
- DOI: https://doi.org/10.3390/s21216975
- DOI: https://doi.org/10.2493/jjspe.87.987
- [2021] Scale Optimization of Structure from Motion for Structured Light-based All-round 3D MeasurementDOI: https://doi.org/10.1109/smc52423.2021.9658793
- DOI: https://doi.org/10.1038/s41598-021-99969-4
- DOI: https://doi.org/10.1080/01691864.2021.2007167
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