{"id":"https://openalex.org/W1892339738","doi":"https://doi.org/10.1109/icra.2015.7139361","title":"Real-time grasp detection using convolutional neural networks","display_name":"Real-time grasp detection using convolutional neural networks","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1892339738","doi":"https://doi.org/10.1109/icra.2015.7139361","mag":"1892339738"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2015.7139361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2015.7139361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062301070","display_name":"Joseph Redmon","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Redmon","raw_affiliation_strings":["University of Washington","University of Washington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Washington, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066709641","display_name":"Anelia Angelova","orcid":"https://orcid.org/0000-0003-1822-7943"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anelia Angelova","raw_affiliation_strings":["Google Research","Google Research USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Research USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":916,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1316","last_page":"1322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10868","display_name":"Soft Robotics and Applications","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.8621649146080017},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7986164093017578},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.7326073050498962},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7013838291168213},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6687632203102112},{"id":"https://openalex.org/keywords/rectangle","display_name":"Rectangle","score":0.6361041069030762},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.549697995185852},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5490624308586121},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.5320495367050171},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43561872839927673},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42855846881866455},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37059658765792847},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.36590707302093506},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0893150269985199}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.8621649146080017},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7986164093017578},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7326073050498962},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7013838291168213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6687632203102112},{"id":"https://openalex.org/C2781302577","wikidata":"https://www.wikidata.org/wiki/Q209","display_name":"Rectangle","level":2,"score":0.6361041069030762},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.549697995185852},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5490624308586121},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.5320495367050171},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43561872839927673},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42855846881866455},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37059658765792847},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.36590707302093506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0893150269985199},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2015.7139361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2015.7139361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1187244281","https://openalex.org/W1510186039","https://openalex.org/W1808974695","https://openalex.org/W1820657498","https://openalex.org/W1999156278","https://openalex.org/W2016876803","https://openalex.org/W2041376653","https://openalex.org/W2068730032","https://openalex.org/W2070961462","https://openalex.org/W2073660060","https://openalex.org/W2075274454","https://openalex.org/W2102605133","https://openalex.org/W2106628124","https://openalex.org/W2108598243","https://openalex.org/W2118262422","https://openalex.org/W2123435073","https://openalex.org/W2130306094","https://openalex.org/W2155541015","https://openalex.org/W2156222070","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2295343990","https://openalex.org/W4294375521","https://openalex.org/W6627862999","https://openalex.org/W6650115879","https://openalex.org/W6667689585","https://openalex.org/W6676297131","https://openalex.org/W6679349572","https://openalex.org/W6682778277","https://openalex.org/W6683619738","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2935821188","https://openalex.org/W3185561939","https://openalex.org/W2095705906","https://openalex.org/W4386794497","https://openalex.org/W2975200075","https://openalex.org/W2732308154","https://openalex.org/W1988485990","https://openalex.org/W2801801420","https://openalex.org/W3177406559","https://openalex.org/W2334336442"],"abstract_inverted_index":{"We":[0],"present":[1],"an":[2],"accurate,":[3],"real-time":[4],"approach":[5],"to":[6,20,76],"robotic":[7],"grasp":[8,72],"detection":[9],"based":[10],"on":[11,49,99],"convolutional":[12],"neural":[13],"networks.":[14],"Our":[15,52],"network":[16,53],"performs":[17,95],"single-stage":[18],"regression":[19],"graspable":[21],"bounding":[22],"boxes":[23],"without":[24],"using":[25,85],"standard":[26],"sliding":[27],"window":[28],"or":[29],"region":[30],"proposal":[31],"techniques.":[32],"The":[33,91],"model":[34,78,94],"outperforms":[35],"state-of-the-art":[36],"approaches":[37],"by":[38,84],"14":[39],"percentage":[40],"points":[41],"and":[42,68],"runs":[43],"at":[44],"13":[45],"frames":[46],"per":[47,82],"second":[48],"a":[50,61,70,86,106],"GPU.":[51],"can":[54,102],"simultaneously":[55],"perform":[56],"classification":[57],"so":[58],"that":[59,101],"in":[60,105],"single":[62],"step":[63],"it":[64],"recognizes":[65],"the":[66],"object":[67,83],"finds":[69],"good":[71],"rectangle.":[73],"A":[74],"modification":[75],"this":[77],"predicts":[79],"multiple":[80],"grasps":[81],"locally":[87,92],"constrained":[88,93],"prediction":[89],"mechanism.":[90],"significantly":[96],"better,":[97],"especially":[98],"objects":[100],"be":[103],"grasped":[104],"variety":[107],"of":[108],"ways.":[109]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":65},{"year":2024,"cited_by_count":100},{"year":2023,"cited_by_count":120},{"year":2022,"cited_by_count":131},{"year":2021,"cited_by_count":113},{"year":2020,"cited_by_count":120},{"year":2019,"cited_by_count":114},{"year":2018,"cited_by_count":66},{"year":2017,"cited_by_count":50},{"year":2016,"cited_by_count":17},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}