{"id":"https://openalex.org/W4416196945","doi":"https://doi.org/10.1017/s0263574725102750","title":"Research on robotic grasp detection using improved generative convolution neural network with Gaussian representation","display_name":"Research on robotic grasp detection using improved generative convolution neural network with Gaussian representation","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W4416196945","doi":"https://doi.org/10.1017/s0263574725102750"},"language":"en","primary_location":{"id":"doi:10.1017/s0263574725102750","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s0263574725102750","pdf_url":null,"source":{"id":"https://openalex.org/S92163612","display_name":"Robotica","issn_l":"0263-5747","issn":["0263-5747","1469-8668"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Robotica","raw_type":"journal-article"},"type":"article","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/A5090402448","display_name":"Z. R. Chen Z. R. Chen","orcid":"https://orcid.org/0009-0001-3343-7642"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanglai Chen","raw_affiliation_strings":["Shanghai University","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-3343-7642","affiliations":[{"raw_affiliation_string":"Shanghai University","institution_ids":["https://openalex.org/I113940042"]},{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320169","display_name":"Xu Zhang","orcid":"https://orcid.org/0000-0003-2969-0044"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Zhang","raw_affiliation_strings":["Shanghai University","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University","institution_ids":["https://openalex.org/I113940042"]},{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":null,"display_name":"Dawei Tu","orcid":null},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dawei Tu","raw_affiliation_strings":["Shanghai University","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University","institution_ids":["https://openalex.org/I113940042"]},{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3700082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"43","issue":"12","first_page":"4191","last_page":"4211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.6359999775886536,"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":0.6359999775886536,"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":0.14659999310970306,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.060499999672174454,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.8690000176429749},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5924999713897705},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5534999966621399},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4984999895095825},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48649999499320984},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48019999265670776},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4708000123500824},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4643999934196472},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44589999318122864},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42910000681877136}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.8690000176429749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7554000020027161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7171000242233276},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5924999713897705},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5534999966621399},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48019999265670776},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4666999876499176},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4643999934196472},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3917999863624573},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.38960000872612},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.38510000705718994},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.37779998779296875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34380000829696655},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.31769999861717224},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1017/s0263574725102750","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s0263574725102750","pdf_url":null,"source":{"id":"https://openalex.org/S92163612","display_name":"Robotica","issn_l":"0263-5747","issn":["0263-5747","1469-8668"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Robotica","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1892339738","https://openalex.org/W1999156278","https://openalex.org/W2011792403","https://openalex.org/W2485911221","https://openalex.org/W2736534894","https://openalex.org/W2752782242","https://openalex.org/W2953669419","https://openalex.org/W2962821282","https://openalex.org/W2962875890","https://openalex.org/W2963326767","https://openalex.org/W2963604034","https://openalex.org/W2967869116","https://openalex.org/W3035198432","https://openalex.org/W3044277051","https://openalex.org/W3056171435","https://openalex.org/W3120238879","https://openalex.org/W3129140312","https://openalex.org/W3130885760","https://openalex.org/W4206575323","https://openalex.org/W4210316419","https://openalex.org/W4221167790","https://openalex.org/W4292978855","https://openalex.org/W4312378951","https://openalex.org/W4312695529","https://openalex.org/W4380450805","https://openalex.org/W4381729886","https://openalex.org/W4383502624","https://openalex.org/W4386697105","https://openalex.org/W4386916215","https://openalex.org/W4386967465","https://openalex.org/W4396716951","https://openalex.org/W4402041279","https://openalex.org/W4402610783","https://openalex.org/W4404826483","https://openalex.org/W4407774879"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Grasp":[1],"detection":[2,26],"is":[3,219],"a":[4,80,122,158],"significant":[5],"research":[6],"direction":[7],"in":[8,32,98,145,197,211],"the":[9,21,38,69,93,107,113,119,129,133,141,146,170,206],"field":[10,124],"of":[11,20,95,118],"robotics.":[12],"Traditional":[13],"analysis":[14],"methods":[15],"typically":[16],"require":[17],"prior":[18],"knowledge":[19],"object":[22],"parameters,":[23],"limiting":[24],"grasp":[25,77,89,99,104],"to":[27,87,91,111,131,136,163,172],"structured":[28],"environments":[29],"and":[30,59,75,166,191,215,218],"resulting":[31],"suboptimal":[33],"performance.":[34],"In":[35],"recent":[36],"years,":[37],"generative":[39],"convolutional":[40],"neural":[41],"network":[42],"(GCNN)":[43],"has":[44],"gained":[45],"increasing":[46],"attention,":[47],"but":[48],"they":[49],"suffer":[50],"from":[51],"issues":[52],"such":[53],"as":[54,194,196],"insufficient":[55,114],"feature":[56,115,143,160],"extraction":[57,116],"capabilities":[58,117],"redundant":[60,151],"noise.":[61,152],"Therefore,":[62],"we":[63,156],"proposed":[64,207],"an":[65],"improved":[66],"method":[67,208],"for":[68,222],"GCNN,":[70],"aimed":[71],"at":[72,128],"enabling":[73,169],"fast":[74],"accurate":[76],"detection.":[78],"First,":[79],"two-dimensional":[81],"(2D)":[82],"Gaussian":[83],"kernel":[84],"was":[85,126],"introduced":[86,157],"re-encode":[88],"quality":[90],"address":[92,112,154],"issue":[94],"false":[96],"positives":[97],"rectangular":[100],"metrics,":[101],"emphasizing":[102],"high-quality":[103],"poses":[105],"near":[106],"central":[108],"point.":[109],"Additionally,":[110],"shallow":[120],"network,":[121],"receptive":[123],"module":[125,162],"added":[127],"neck":[130],"enhance":[132,167],"network\u2019s":[134],"ability":[135],"extract":[137],"distinctive":[138],"features.":[139],"Furthermore,":[140],"rich":[142],"information":[144],"decoding":[147],"phase":[148],"often":[149],"contains":[150],"To":[153],"this,":[155],"global-local":[159],"fusion":[161],"suppress":[164],"noise":[165],"features,":[168],"model":[171],"focus":[173],"more":[174],"on":[175,184],"target":[176],"information.":[177],"Finally,":[178],"relevant":[179],"evaluation":[180],"experiments":[181],"were":[182],"conducted":[183],"public":[185],"grasping":[186,200],"datasets,":[187],"including":[188],"Cornell,":[189],"Jacquard,":[190],"GraspNet-1":[192],"Billion,":[193],"well":[195],"real-world":[198],"robotic":[199,223],"scenarios.":[201],"All":[202],"results":[203],"showed":[204],"that":[205],"performs":[209],"excellently":[210],"both":[212],"prediction":[213],"accuracy":[214],"inference":[216],"speed":[217],"practically":[220],"feasible":[221],"grasping.":[224]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-11-14T00:00:00"}