{"id":"https://openalex.org/W3086512743","doi":"https://doi.org/10.1177/0278364920954896","title":"Combining learned and analytical models for predicting action effects from sensory data","display_name":"Combining learned and analytical models for predicting action effects from sensory data","publication_year":2020,"publication_date":"2020-09-12","ids":{"openalex":"https://openalex.org/W3086512743","doi":"https://doi.org/10.1177/0278364920954896","mag":"3086512743"},"language":"en","primary_location":{"id":"doi:10.1177/0278364920954896","is_oa":true,"landing_page_url":"https://doi.org/10.1177/0278364920954896","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/0278364920954896","source":{"id":"https://openalex.org/S73484101","display_name":"The International Journal of Robotics Research","issn_l":"0278-3649","issn":["0278-3649","1741-3176"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International Journal of Robotics Research","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://journals.sagepub.com/doi/pdf/10.1177/0278364920954896","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Alina Kloss","orcid":"https://orcid.org/0000-0001-5793-1875"},"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":true,"raw_author_name":"Alina Kloss","raw_affiliation_strings":["Autonomous Motion Department, MPI for Intelligent Systems, T\u00fcbingen, Germany"],"affiliations":[{"raw_affiliation_string":"Autonomous Motion Department, MPI for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Stefan Schaal","orcid":null},"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stefan Schaal","raw_affiliation_strings":["Computational Learning and Motor Control Lab, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Computational Learning and Motor Control Lab, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jeannette Bohg","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeannette Bohg","raw_affiliation_strings":["Department of Computer Science, Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210135521"],"apc_list":null,"apc_paid":null,"fwci":2.38,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.88777203,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"41","issue":"8","first_page":"778","last_page":"797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.800599992275238,"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":0.800599992275238,"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/T12290","display_name":"Human Motion and Animation","score":0.02850000001490116,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.028200000524520874,"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/leverage","display_name":"Leverage (statistics)","score":0.646399974822998},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5546000003814697},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5268999934196472},{"id":"https://openalex.org/keywords/sensory-system","display_name":"Sensory system","score":0.5135999917984009},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49950000643730164},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4927000105381012},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.45170000195503235},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44040000438690186},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4345000088214874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7132999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6845999956130981},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.646399974822998},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6089000105857849},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5546000003814697},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5268999934196472},{"id":"https://openalex.org/C94487597","wikidata":"https://www.wikidata.org/wiki/Q11101","display_name":"Sensory system","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49950000643730164},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44040000438690186},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.38850000500679016},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C11207580","wikidata":"https://www.wikidata.org/wiki/Q5266817","display_name":"Developmental robotics","level":4,"score":0.2572999894618988},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1177/0278364920954896","is_oa":true,"landing_page_url":"https://doi.org/10.1177/0278364920954896","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/0278364920954896","source":{"id":"https://openalex.org/S73484101","display_name":"The International Journal of Robotics Research","issn_l":"0278-3649","issn":["0278-3649","1741-3176"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International Journal of Robotics Research","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1710.04102","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1710.04102","pdf_url":"https://arxiv.org/pdf/1710.04102","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1177/0278364920954896","is_oa":true,"landing_page_url":"https://doi.org/10.1177/0278364920954896","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/0278364920954896","source":{"id":"https://openalex.org/S73484101","display_name":"The International Journal of Robotics Research","issn_l":"0278-3649","issn":["0278-3649","1741-3176"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International Journal of Robotics Research","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322434","display_name":"Max-Planck-Gesellschaft","ror":"https://ror.org/01hhn8329"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3086512743.pdf","grobid_xml":"https://content.openalex.org/works/W3086512743.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1164749991","https://openalex.org/W1973600975","https://openalex.org/W1998179438","https://openalex.org/W2029342956","https://openalex.org/W2050708324","https://openalex.org/W2060554619","https://openalex.org/W2083561185","https://openalex.org/W2099951169","https://openalex.org/W2119104235","https://openalex.org/W2149175962","https://openalex.org/W2461838149","https://openalex.org/W2962867039","https://openalex.org/W2963030226","https://openalex.org/W2963149945","https://openalex.org/W2963892386"],"related_works":[],"abstract_inverted_index":{"One":[0],"of":[1,14,62,80,112,120,140,168,206,220],"the":[2,12,20,63,78,108,118,138,166,187,207,221],"most":[3],"basic":[4],"skills":[5],"a":[6,29,155,160,169,183,211,226],"robot":[7],"should":[8],"possess":[9],"is":[10,66],"predicting":[11,117],"effect":[13,79],"physical":[15,82,144,243],"interactions":[16,83],"with":[17,194,225],"objects":[18],"in":[19],"environment.":[21],"This":[22],"enables":[23],"optimal":[24],"action":[25],"selection":[26],"to":[27,52,136,173,241],"reach":[28],"certain":[30],"goal":[31],"state.":[32],"Traditionally,":[33],"dynamics":[34],"are":[35],"approximated":[36],"by":[37],"physics-based":[38],"analytical":[39,129,157,188],"models.":[40],"These":[41],"models":[42,97,132],"rely":[43],"on":[44,123,210],"specific":[45],"state":[46],"representations":[47],"that":[48,75],"may":[49],"be":[50,134],"hard":[51],"obtain":[53],"from":[54,85],"raw":[55,175],"sensory":[56,86,124],"data,":[57],"especially":[58],"if":[59],"no":[60],"knowledge":[61],"object":[64],"shape":[65],"assumed.":[67],"More":[68],"recently,":[69],"we":[70,106,147],"have":[71],"seen":[72],"learning":[73,114],"approaches":[74,115],"can":[76,133],"predict":[77],"complex":[81],"directly":[84],"input.":[87],"It":[88],"is,":[89],"however,":[90],"an":[91],"open":[92],"question":[93],"how":[94,128],"far":[95],"these":[96],"generalize":[98],"beyond":[99],"their":[100],"training":[101,235],"data.":[102],"In":[103],"this":[104,192],"work,":[105],"investigate":[107],"advantages":[109,219],"and":[110,126,130,159,190,201,237],"limitations":[111],"neural-network-based":[113],"for":[116,151,186,198],"effects":[119],"actions":[121],"based":[122],"input":[125],"show":[127],"learned":[131],"combined":[135],"leverage":[137],"best":[139],"both":[141],"worlds.":[142],"As":[143],"interaction":[145],"task,":[146],"use":[148,167],"planar":[149],"pushing,":[150],"which":[152],"there":[153],"exists":[154],"well-known":[156],"model":[158,189],"large":[161,213],"real-world":[162,214],"dataset.":[163],"We":[164],"propose":[165],"convolutional":[170],"neural":[171,196,228],"network":[172],"convert":[174],"depth":[176],"images":[177],"or":[178],"organized":[179],"point":[180],"clouds":[181],"into":[182],"suitable":[184],"representation":[185],"compare":[191],"approach":[193,209],"using":[195],"networks":[197],"both,":[199],"perception":[200],"prediction.":[202],"A":[203],"systematic":[204],"evaluation":[205],"proposed":[208],"very":[212],"dataset":[215],"shows":[216],"two":[217],"main":[218],"hybrid":[222],"architecture.":[223],"Compared":[224],"pure":[227],"network,":[229],"it":[230],"significantly":[231],"(i)":[232],"reduces":[233],"required":[234],"data":[236],"(ii)":[238],"improves":[239],"generalization":[240],"novel":[242],"interaction.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2020-09-21T00:00:00"}