ログイン
言語:

WEKO3

  • トップ
  • ランキング
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

{"_buckets": {"deposit": "98c1e392-4fd1-4b9b-b84f-fdd10327fe06"}, "_deposit": {"id": "24556", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "24556"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00024556", "sets": ["322"]}, "author_link": ["72693", "72694", "72695", "72696", "72697"], "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2017-04", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "2", "bibliographicPageEnd": "483", "bibliographicPageStart": "468", "bibliographicVolumeNumber": "33", "bibliographic_titles": [{"bibliographic_title": "IEEE Transactions on Robotics", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "In many engineering problems, including those related to robotics, optimization of the control policy for multiple conflicting criteria is required. However, this can be very challenging because of the existence of noise, which may be input dependent or heteroscedastic, and restrictions regarding the number of evaluations owing to the costliness of the experiments in terms of time and/or money. This paper presents a multiobjective optimization algorithm for expensive-to-evaluate noisy functions for robotics. We present a method for model selection between heteroscedastic and standard homoscedastic Gaussian process regression techniques to create suitable surrogate functions from noisy samples, and to find the point to be observed at the next step. This algorithm is compared against an existing multiobjective optimization algorithm, and then used to optimize the speed and head stability of the sidewinding gait of a snake robot.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://doi.org/10.1109/TRO.2016.2632739"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/26774"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "IEEE", "subitem_publisher_language": "en"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1109/TRO.2016.2632739", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "“© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”", "subitem_rights_language": "en"}]}, "item_10_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1552-3098", "subitem_source_identifier_type": "PISSN"}]}, "item_1615787544753": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_ab4af688f83e57aa", "subitem_version_type": "AM"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Ariizumi, Ryo", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "72693", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Tesch, Matthew", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "72694", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Kato, Kenta", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "72695", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Choset, Howie", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "72696", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Matsuno, Fumitoshi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "72697", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-22"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "draft.pdf", "filesize": [{"value": "1.6 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 1600000.0, "url": {"label": "draft.pdf", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/24556/files/draft.pdf"}, "version_id": "441b32fc-8a17-46ba-8e81-969cae87f317"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Multiobjective Optimization Based on Expensive Robotic Experiments under Heteroscedastic Noise", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Multiobjective Optimization Based on Expensive Robotic Experiments under Heteroscedastic Noise", "subitem_title_language": "en"}]}, "item_type_id": "10", "owner": "1", "path": ["322"], "permalink_uri": "http://hdl.handle.net/2237/26774", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2017-07-05"}, "publish_date": "2017-07-05", "publish_status": "0", "recid": "24556", "relation": {}, "relation_version_is_last": true, "title": ["Multiobjective Optimization Based on Expensive Robotic Experiments under Heteroscedastic Noise"], "weko_shared_id": -1}
  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

Multiobjective Optimization Based on Expensive Robotic Experiments under Heteroscedastic Noise

http://hdl.handle.net/2237/26774
http://hdl.handle.net/2237/26774
8eb72820-d01a-431a-ab0d-65b86edaad57
名前 / ファイル ライセンス アクション
draft.pdf draft.pdf (1.6 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2017-07-05
タイトル
タイトル Multiobjective Optimization Based on Expensive Robotic Experiments under Heteroscedastic Noise
言語 en
著者 Ariizumi, Ryo

× Ariizumi, Ryo

WEKO 72693

en Ariizumi, Ryo

Search repository
Tesch, Matthew

× Tesch, Matthew

WEKO 72694

en Tesch, Matthew

Search repository
Kato, Kenta

× Kato, Kenta

WEKO 72695

en Kato, Kenta

Search repository
Choset, Howie

× Choset, Howie

WEKO 72696

en Choset, Howie

Search repository
Matsuno, Fumitoshi

× Matsuno, Fumitoshi

WEKO 72697

en Matsuno, Fumitoshi

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 “© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
抄録
内容記述 In many engineering problems, including those related to robotics, optimization of the control policy for multiple conflicting criteria is required. However, this can be very challenging because of the existence of noise, which may be input dependent or heteroscedastic, and restrictions regarding the number of evaluations owing to the costliness of the experiments in terms of time and/or money. This paper presents a multiobjective optimization algorithm for expensive-to-evaluate noisy functions for robotics. We present a method for model selection between heteroscedastic and standard homoscedastic Gaussian process regression techniques to create suitable surrogate functions from noisy samples, and to find the point to be observed at the next step. This algorithm is compared against an existing multiobjective optimization algorithm, and then used to optimize the speed and head stability of the sidewinding gait of a snake robot.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/TRO.2016.2632739
ISSN
収録物識別子タイプ PISSN
収録物識別子 1552-3098
書誌情報 en : IEEE Transactions on Robotics

巻 33, 号 2, p. 468-483, 発行日 2017-04
著者版フラグ
値 author
URI
識別子 http://doi.org/10.1109/TRO.2016.2632739
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/26774
識別子タイプ HDL
戻る
0
views
See details
Views

Versions

Ver.1 2021-03-01 14:00:26.868442
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3