{"id":"https://openalex.org/W7161992509","doi":"https://doi.org/10.48550/arxiv.2605.21258","title":"Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation","display_name":"Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161992509","doi":"https://doi.org/10.48550/arxiv.2605.21258"},"language":"en","primary_location":{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-172647","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-172647","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.21258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125662731","display_name":"Yicheng Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136641224","display_name":"Jiaxu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiaxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136618905","display_name":"Junhao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Junhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125648386","display_name":"Zesen Gan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gan, Zesen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136710795","display_name":"Junhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Junhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136693049","display_name":"Qiang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049574394","display_name":"Jingkai Sun","orcid":"https://orcid.org/0000-0002-1032-2957"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jingkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136684010","display_name":"Jiahang Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Jiahang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125661405","display_name":"Mingyuan Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Mingyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136626033","display_name":"Xiangyu Yue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125644889","display_name":"Qiming Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Qiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6237000226974487,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6237000226974487,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10653","display_name":"Robot Manipulation and Learning","score":0.18140000104904175,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07400000095367432,"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/rendering","display_name":"Rendering (computer graphics)","score":0.5849999785423279},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5519000291824341},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5440000295639038},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.48069998621940613},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4683000147342682},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.4332999885082245},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4027999937534332},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.3781999945640564},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.3741999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6603999733924866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6427000164985657},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5519000291824341},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5440000295639038},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.48069998621940613},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4683000147342682},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3741999864578247},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3271999955177307},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3037000000476837},{"id":"https://openalex.org/C111151474","wikidata":"https://www.wikidata.org/wiki/Q1653368","display_name":"iCub","level":4,"score":0.3003999888896942},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C173552908","wikidata":"https://www.wikidata.org/wiki/Q1366289","display_name":"Graphics pipeline","level":4,"score":0.29190000891685486},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2727999985218048},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.2711000144481659},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-172647","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-172647","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"},{"id":"doi:10.48550/arxiv.2605.21258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21258","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.21258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21258","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Current":[0],"3D-aware":[1],"pretraining":[2,56],"methods":[3],"for":[4],"embodied":[5],"perception":[6],"and":[7,45,85,110,167,179,183],"manipulation":[8],"are":[9],"largely":[10],"built":[11],"on":[12,164],"differentiable":[13],"rendering":[14,143],"frameworks,":[15],"producing":[16],"either":[17],"fully":[18,23],"implicit":[19,118],"neural":[20],"fields":[21],"or":[22],"explicit":[24,32,36,125],"geometric":[25],"primitives.":[26],"Implicit":[27],"representations,":[28],"while":[29,152],"expressive,":[30],"lack":[31],"structural":[33,97,122],"cues,":[34],"whereas":[35],"ones":[37],"preserve":[38],"geometry":[39,104],"but":[40,105],"suffer":[41],"from":[42],"resolution":[43],"limits":[44],"weak":[46],"generalization.":[47],"To":[48],"address":[49],"these":[50],"limitations,":[51],"we":[52,66,137],"propose":[53],"a":[54,60,68,78,88,139,168],"novel":[55],"framework":[57,198],"that":[58,145,193],"learns":[59],"hybrid":[61],"representation-structural":[62],"latent":[63,70,75,94,160],"points.":[64],"Specifically,":[65],"insert":[67],"point-wise":[69,83],"variational":[71],"autoencoder":[72],"into":[73],"the":[74,115,121,158],"space":[76],"of":[77,117,124,196],"point-cloud":[79],"autoencoder,":[80],"jointly":[81],"regularizing":[82],"features":[84],"coordinates":[86],"toward":[87],"Gaussian":[89],"prior.":[90],"The":[91],"resulting":[92],"compact":[93],"preserves":[95],"coarse":[96],"tendencies,":[98],"which":[99],"do":[100],"not":[101],"encode":[102],"precise":[103],"capture":[106],"richer":[107],"rough":[108],"shape":[109],"semantic":[111],"information,":[112],"effectively":[113],"combining":[114],"expressiveness":[116],"representations":[119],"with":[120],"priors":[123],"ones.":[126],"In":[127],"addition,":[128],"informed":[129],"by":[130],"shared":[131],"design":[132],"choices":[133],"in":[134,174],"prior":[135],"work,":[136],"develop":[138],"streamlined,":[140],"efficient":[141],"3DGS-based":[142],"pipeline":[144],"is":[146,199],"deliberately":[147],"kept":[148],"lightweight,":[149],"improving":[150],"efficiency":[151],"leaving":[153],"greater":[154],"representational":[155],"capacity":[156],"to":[157,181,201],"front-end":[159],"module.":[161],"Extensive":[162],"evaluations":[163],"RLBench,":[165],"ManiSkill2,":[166],"real-robot":[169],"platform":[170],"demonstrate":[171],"consistent":[172],"gains":[173],"task":[175],"success,":[176],"sample":[177],"efficiency,":[178],"robustness":[180],"viewpoint":[182],"scene":[184],"variations":[185],"over":[186],"strong":[187],"baselines.":[188],"Ablation":[189],"studies":[190],"further":[191],"confirm":[192],"each":[194],"component":[195],"our":[197],"critical":[200],"overall":[202],"performance.":[203]},"counts_by_year":[],"updated_date":"2026-07-07T14:30:12.667765","created_date":"2026-05-22T00:00:00"}