{"id":"https://openalex.org/W2417956634","doi":"https://doi.org/10.1109/icra.2016.7487367","title":"Optimizing for what matters: The top grasp hypothesis","display_name":"Optimizing for what matters: The top grasp hypothesis","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2417956634","doi":"https://doi.org/10.1109/icra.2016.7487367","mag":"2417956634"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2016.7487367","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5039293409","display_name":"Daniel Kappler","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Kappler","raw_affiliation_strings":["Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029642293","display_name":"Stefan Schaal","orcid":"https://orcid.org/0000-0001-5660-1874"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Stefan Schaal","raw_affiliation_strings":["Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany","Computational Learning and Motor Control lab at the University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]},{"raw_affiliation_string":"Computational Learning and Motor Control lab at the University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021676288","display_name":"Jeannette Bohg","orcid":"https://orcid.org/0000-0002-4921-7193"},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jeannette Bohg","raw_affiliation_strings":["Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2167","last_page":"2174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9998000264167786,"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":0.9998000264167786,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9063848257064819},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7891796231269836},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7724237442016602},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6950510740280151},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6380297541618347},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.622450590133667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6056331992149353},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5510684251785278},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5184228420257568},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.47195759415626526},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12150514125823975}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.9063848257064819},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7891796231269836},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7724237442016602},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6950510740280151},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6380297541618347},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.622450590133667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6056331992149353},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5510684251785278},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5184228420257568},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.47195759415626526},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12150514125823975},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2016.7487367","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.mpg.de:item_2394976","is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-002D-19C8-D","pdf_url":null,"source":{"id":"https://openalex.org/S4306400654","display_name":"MPG.PuRe (Max Planck Society)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA 2016)","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W46565623","https://openalex.org/W1249953932","https://openalex.org/W1503925285","https://openalex.org/W1546411676","https://openalex.org/W1892339738","https://openalex.org/W1999156278","https://openalex.org/W2015034312","https://openalex.org/W2033223309","https://openalex.org/W2036637075","https://openalex.org/W2041376653","https://openalex.org/W2047221353","https://openalex.org/W2058475745","https://openalex.org/W2085949256","https://openalex.org/W2104588723","https://openalex.org/W2106628124","https://openalex.org/W2108862644","https://openalex.org/W2123296323","https://openalex.org/W2123435073","https://openalex.org/W2143331230","https://openalex.org/W2162000109","https://openalex.org/W2409544781","https://openalex.org/W3112422759","https://openalex.org/W6632670727","https://openalex.org/W6662223031"],"related_works":["https://openalex.org/W2163296013","https://openalex.org/W2743859443","https://openalex.org/W2326995835","https://openalex.org/W2657478029","https://openalex.org/W2080808138","https://openalex.org/W2626503888","https://openalex.org/W4387962997","https://openalex.org/W2129483190","https://openalex.org/W2119384858","https://openalex.org/W2182204190"],"abstract_inverted_index":{"In":[0,152],"this":[1,77,136,146],"paper,":[2],"we":[3,54,117,139,142,155,158],"consider":[4],"the":[5,20,29,34,44,102,119,133],"problem":[6,30],"of":[7,10,19,31,135],"robotic":[8],"grasping":[9],"objects":[11],"when":[12,46],"only":[13,56],"partial":[14,59],"and":[15,79,131],"noisy":[16,66],"sensor":[17],"data":[18,126],"environment":[21],"is":[22,42,92,110],"available.":[23],"We":[24,85,114],"are":[25],"specifically":[26],"interested":[27],"in":[28,106],"reliably":[32],"selecting":[33],"best":[35],"hypothesis":[36,108],"from":[37,62],"a":[38,58,87,95,107,164],"whole":[39],"set.":[40],"This":[41,98],"commonly":[43],"case":[45],"trying":[47],"to":[48,74,123],"grasp":[49,76,105],"an":[50],"object":[51],"for":[52],"which":[53,82],"can":[55,143,159],"observe":[57],"point":[60],"cloud":[61],"one":[63],"viewpoint":[64],"through":[65],"sensors.":[67],"There":[68],"will":[69,83],"be":[70],"many":[71],"possible":[72],"ways":[73],"successfully":[75],"object,":[78],"even":[80],"more":[81],"fail.":[84],"propose":[86],"supervised":[88],"learning":[89],"method":[90],"that":[91,101,127,157],"trained":[93],"with":[94,125,148],"ranking":[96,121],"loss.":[97],"explicitly":[99],"encourages":[100],"top-ranked":[103],"training":[104],"set":[109],"also":[111],"positively":[112],"labeled.":[113],"show":[115,140,156],"how":[116,141],"adapt":[118],"standard":[120],"loss":[122,147],"work":[124],"has":[128],"binary":[129],"labels":[130],"explain":[132],"benefits":[134],"formulation.":[137],"Additionally,":[138],"efficiently":[144],"optimize":[145],"stochastic":[149],"gradient":[150],"descent.":[151],"quantitative":[153],"experiments,":[154],"outperform":[160],"previous":[161],"models":[162],"by":[163],"large":[165],"margin.":[166]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
