{"id":"https://openalex.org/W4414431927","doi":"https://doi.org/10.1109/case58245.2025.11163903","title":"Learning Pixel-Wise Suction Grasp Representation for Cluttered Environments","display_name":"Learning Pixel-Wise Suction Grasp Representation for Cluttered Environments","publication_year":2025,"publication_date":"2025-08-17","ids":{"openalex":"https://openalex.org/W4414431927","doi":"https://doi.org/10.1109/case58245.2025.11163903"},"language":"en","primary_location":{"id":"doi:10.1109/case58245.2025.11163903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case58245.2025.11163903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)","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/A5100348274","display_name":"Yi-Ting Chen","orcid":"https://orcid.org/0009-0000-6537-8926"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiting Chen","raw_affiliation_strings":["Rice University,Department of Computer Science,Houston,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rice University,Department of Computer Science,Houston,USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023501802","display_name":"Ahmet E. Tekden","orcid":"https://orcid.org/0000-0002-1272-0756"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ahmet Tekden","raw_affiliation_strings":["Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339704","display_name":"Miao Li","orcid":"https://orcid.org/0000-0002-2244-2104"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao Li","raw_affiliation_strings":["Wuhan University,Institute of Technological Sciences,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,Institute of Technological Sciences,Wuhan,China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048122691","display_name":"Dimitrios Kanoulas","orcid":"https://orcid.org/0000-0002-3684-1472"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dimitrios Kanoulas","raw_affiliation_strings":["University College London,Department of Computer Science,London,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College London,Department of Computer Science,London,United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015300584","display_name":"Yasemin Bekiroglu","orcid":"https://orcid.org/0000-0002-2597-6013"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Yasemin Bekiroglu","raw_affiliation_strings":["Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3488","last_page":"3493"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.803600013256073,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.803600013256073,"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"}},{"id":"https://openalex.org/T13382","display_name":"Robotics and Automated Systems","score":0.7870000004768372,"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":0.7512999773025513,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.9189000129699707},{"id":"https://openalex.org/keywords/grippers","display_name":"Grippers","score":0.618399977684021},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.45969998836517334},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/suction-cup","display_name":"Suction cup","score":0.42980000376701355},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4244999885559082},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.39910000562667847},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.388700008392334},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.9189000129699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6796000003814697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6489999890327454},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6438999772071838},{"id":"https://openalex.org/C2775960376","wikidata":"https://www.wikidata.org/wiki/Q1435859","display_name":"Grippers","level":2,"score":0.618399977684021},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45100000500679016},{"id":"https://openalex.org/C87321777","wikidata":"https://www.wikidata.org/wiki/Q635971","display_name":"Suction cup","level":2,"score":0.42980000376701355},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/case58245.2025.11163903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case58245.2025.11163903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10215999","is_oa":false,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10215999/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"In: 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE). (pp. pp. 3488-3493). IEEE: Los Angeles, CA, USA. (2025)","raw_type":"Proceedings paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Robotic":[0],"vacuum":[1],"grippers":[2],"provide":[3],"distinct":[4],"advantages":[5],"for":[6,19,140],"handling":[7],"objects":[8],"with":[9,154],"complex":[10],"geometries":[11],"or":[12,40],"compliant":[13],"materials.":[14],"Existing":[15],"data":[16,36,130,139],"annotation":[17,33,107],"methods":[18],"suction":[20,69,102],"grasp":[21,26,70,76,93,103,118,141,160],"planning":[22,161],"typically":[23],"sample":[24,91],"object-level":[25],"labels":[27],"and":[28,54,117,137,157],"transfer":[29],"them":[30],"to":[31,51,90,100,131],"scene-level":[32],"on":[34],"sensor":[35,80,115],"(e.g.,":[37],"depth":[38,129],"images":[39],"point":[41],"clouds).":[42],"However,":[43],"this":[44],"process":[45],"suffers":[46],"from":[47],"sparse":[48],"annotations":[49,151],"due":[50],"random":[52],"sampling":[53],"label":[55],"domain":[56],"shifts,":[57],"degrading":[58],"downstream":[59],"model":[60,162],"performance.":[61],"To":[62],"overcome":[63],"these":[64],"limitations,":[65],"we":[66,121],"propose":[67],"a":[68,97,110,123],"evaluation":[71],"framework":[72],"that":[73,127,148],"directly":[74],"assesses":[75],"feasibility":[77],"at":[78],"every":[79],"pixel.":[81],"Our":[82],"approach":[83],"introduces":[84],"an":[85],"orthographic":[86],"ray":[87],"projection":[88],"module":[89],"pixel-aligned":[92,106],"poses,":[94],"followed":[95],"by":[96],"physics-informed":[98],"metric":[99],"evaluate":[101],"quality.":[104],"This":[105],"pipeline":[108],"ensures":[109],"direct":[111],"bijective":[112],"mapping":[113],"between":[114],"pixels":[116],"labels.":[119],"Additionally,":[120],"present":[122],"modular":[124],"robotic":[125],"system":[126],"utilizes":[128],"perform":[132],"object-agnostic":[133],"seal":[134],"map":[135],"prediction":[136],"RGB":[138],"pose":[142],"refinement.":[143],"Real-world":[144],"robot":[145],"experiments":[146],"demonstrate":[147],"our":[149],"pixel-wise":[150],"align":[152],"well":[153],"practical":[155],"scenarios,":[156],"the":[158],"learned":[159],"outperforms":[163],"existing":[164],"baselines.":[165]},"counts_by_year":[],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}