{"id":"https://openalex.org/W3205536929","doi":"https://doi.org/10.1109/humanoids47582.2021.9555682","title":"Feature-based Deep Learning of Proprioceptive Models for Robotic Force Estimation","display_name":"Feature-based Deep Learning of Proprioceptive Models for Robotic Force Estimation","publication_year":2021,"publication_date":"2021-07-19","ids":{"openalex":"https://openalex.org/W3205536929","doi":"https://doi.org/10.1109/humanoids47582.2021.9555682","mag":"3205536929"},"language":"en","primary_location":{"id":"doi:10.1109/humanoids47582.2021.9555682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/humanoids47582.2021.9555682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)","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/A5089638276","display_name":"Erik Berger","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Erik Berger","raw_affiliation_strings":["Digital Enterprise & Digital Services, Siemens AG, Sch\u00fcutzenstr. 4-10, Leipzig, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Enterprise & Digital Services, Siemens AG, Sch\u00fcutzenstr. 4-10, Leipzig, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039724127","display_name":"Alexander Uhlig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Uhlig","raw_affiliation_strings":["The SQLNet Company GmbH, Philipp-Reis-Str. 11b, Leipzig, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The SQLNet Company GmbH, Philipp-Reis-Str. 11b, Leipzig, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2931,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.53874738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"441","issue":null,"first_page":"128","last_page":"134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9951000213623047,"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/computer-science","display_name":"Computer science","score":0.690735936164856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6757240891456604},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5840306878089905},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5834780335426331},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.538986325263977},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41140806674957275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3768298029899597},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1546439528465271},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0543789267539978}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.690735936164856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6757240891456604},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5840306878089905},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5834780335426331},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.538986325263977},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41140806674957275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3768298029899597},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1546439528465271},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0543789267539978},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/humanoids47582.2021.9555682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/humanoids47582.2021.9555682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W194249466","https://openalex.org/W2024472792","https://openalex.org/W2024828917","https://openalex.org/W2051811141","https://openalex.org/W2055478070","https://openalex.org/W2062896400","https://openalex.org/W2064675550","https://openalex.org/W2182353144","https://openalex.org/W2295598076","https://openalex.org/W2330820318","https://openalex.org/W2562290096","https://openalex.org/W2762248135","https://openalex.org/W2802314367","https://openalex.org/W3004255398","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W2576994247","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W2608353378","https://openalex.org/W4383066092","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Safe":[0],"and":[1,29,65,112,139,159,184],"meaningful":[2],"interaction":[3],"with":[4,43,104,129,189],"robotic":[5,102],"systems":[6],"during":[7,137],"behavior":[8,103,141],"execution":[9],"requires":[10],"accurate":[11,37],"sensing":[12,38],"capabilities.":[13],"This":[14,92],"can":[15],"be":[16],"achieved":[17],"by":[18,70,117,203],"the":[19,107,118,121,134,180,190],"usage":[20],"of":[21,47,83,106,133,156,170,182],"force-torque":[22],"sensors":[23,55,125],"which":[24,87,173],"are":[25,56,126,164],"often":[26,77],"heavy,":[27],"expensive,":[28],"require":[30],"an":[31],"additional":[32],"power":[33],"supply.":[34],"Consequently,":[35],"providing":[36],"capabilities":[39],"to":[40,59,148,177],"lightweight":[41],"robots,":[42],"a":[44,50,80,89,95,153,175,194],"limited":[45],"amount":[46,181],"load,":[48],"is":[49,88,146],"challenging":[51],"task.":[52],"Furthermore,":[53],"such":[54],"not":[57],"able":[58],"distinguish":[60,179],"between":[61],"task-specific":[62],"regular":[63,138],"forces":[64,110,114,136],"external":[66],"influences":[67],"as":[68],"induced":[69],"human":[71],"co-workers.":[72],"To":[73],"solve":[74],"this,":[75],"robots":[76],"rely":[78],"on":[79],"large":[81],"number":[82],"manually":[84],"generated":[85],"rules":[86],"time-consuming":[90],"procedure.":[91],"paper":[93],"presents":[94],"data-driven":[96],"machine":[97],"learning":[98,169],"approach":[99],"that":[100,151],"enhances":[101],"estimates":[105],"expected":[108],"proprioceptive":[109,171],"(intrinsic)":[111],"unexpected":[113],"(extrinsic)":[115],"exerted":[116],"environment.":[119],"First,":[120],"robot\u2019s":[122],"common":[123],"internal":[124],"recorded":[127],"together":[128],"ground":[130],"truth":[131],"measurements":[132],"actual":[135],"perturbed":[140],"executions.":[142],"The":[143],"resulting":[144],"data":[145],"used":[147],"generate":[149],"features":[150,163],"contain":[152],"compact":[154],"representation":[155],"behavior-specific":[157],"intrinsic":[158,183],"extrinsic":[160,185],"fluctuations.":[161],"Those":[162],"then":[165],"utilized":[166],"for":[167],"deep":[168],"models":[172],"enables":[174],"robot":[176,192],"accurately":[178],"forces.":[186],"Experiments":[187],"performed":[188],"UR5":[191],"show":[193],"substantial":[195],"improvement":[196],"in":[197],"accuracy":[198],"over":[199],"force":[200],"values":[201],"provided":[202],"previous":[204],"research.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
