{"id":"https://openalex.org/W4285102401","doi":"https://doi.org/10.1109/icra46639.2022.9811974","title":"Cost-Effective Sensing for Goal Inference: A Model Predictive Approach","display_name":"Cost-Effective Sensing for Goal Inference: A Model Predictive Approach","publication_year":2022,"publication_date":"2022-05-23","ids":{"openalex":"https://openalex.org/W4285102401","doi":"https://doi.org/10.1109/icra46639.2022.9811974"},"language":"en","primary_location":{"id":"doi:10.1109/icra46639.2022.9811974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra46639.2022.9811974","pdf_url":null,"source":{"id":"https://openalex.org/S4363607759","display_name":"2022 International Conference on Robotics and Automation (ICRA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5101453870","display_name":"Ran Tian","orcid":"https://orcid.org/0000-0003-4435-580X"},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ran Tian","raw_affiliation_strings":["UC Berkeley"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC Berkeley","institution_ids":["https://openalex.org/I134446601","https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100341015","display_name":"Nan Li","orcid":"https://orcid.org/0000-0001-7928-8796"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nan Li","raw_affiliation_strings":["University of Michigan,Ann Arbor","University of Michigan, Ann Arbor"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan,Ann Arbor","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074685221","display_name":"Anouck Girard","orcid":"https://orcid.org/0000-0003-3410-7271"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anouck Girard","raw_affiliation_strings":["University of Michigan,Ann Arbor","University of Michigan, Ann Arbor"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan,Ann Arbor","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078022728","display_name":"Ilya Kolmanovsky","orcid":"https://orcid.org/0000-0002-7225-4160"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ilya Kolmanovsky","raw_affiliation_strings":["University of Michigan,Ann Arbor","University of Michigan, Ann Arbor"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan,Ann Arbor","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064077634","display_name":"Masayoshi Tomizuka","orcid":"https://orcid.org/0000-0003-0206-6639"},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Masayoshi Tomizuka","raw_affiliation_strings":["UC Berkeley"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC Berkeley","institution_ids":["https://openalex.org/I134446601","https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08362956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"6451","last_page":"6457"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9866999983787537,"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":0.9765999913215637,"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/computer-science","display_name":"Computer science","score":0.7923247814178467},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7450028657913208},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.6021243929862976},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5694563984870911},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48593583703041077},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4698715806007385},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.4529217779636383},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4404746890068054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44004881381988525},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.42928075790405273},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4242020845413208},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.15511885285377502},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09916448593139648}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7923247814178467},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7450028657913208},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6021243929862976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5694563984870911},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48593583703041077},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4698715806007385},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.4529217779636383},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4404746890068054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44004881381988525},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.42928075790405273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4242020845413208},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.15511885285377502},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09916448593139648},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra46639.2022.9811974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra46639.2022.9811974","pdf_url":null,"source":{"id":"https://openalex.org/S4363607759","display_name":"2022 International Conference on Robotics and Automation (ICRA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W266133316","https://openalex.org/W1581800915","https://openalex.org/W1995875735","https://openalex.org/W2009659616","https://openalex.org/W2098774185","https://openalex.org/W2123487311","https://openalex.org/W2161819990","https://openalex.org/W2165030081","https://openalex.org/W2418222507","https://openalex.org/W2555139634","https://openalex.org/W2569616450","https://openalex.org/W2767734520","https://openalex.org/W2964179964","https://openalex.org/W2970377754","https://openalex.org/W2986445670","https://openalex.org/W3016228756","https://openalex.org/W3035021982","https://openalex.org/W3091831213","https://openalex.org/W3207966700","https://openalex.org/W4300799055","https://openalex.org/W6674884181","https://openalex.org/W6729937758","https://openalex.org/W6740801417","https://openalex.org/W6746136750","https://openalex.org/W6765008394","https://openalex.org/W6776193808"],"related_works":["https://openalex.org/W143502885","https://openalex.org/W42113618","https://openalex.org/W2103468410","https://openalex.org/W2480115405","https://openalex.org/W3197542402","https://openalex.org/W1856228368","https://openalex.org/W2971527398","https://openalex.org/W82829784","https://openalex.org/W1929207905","https://openalex.org/W1025150868"],"abstract_inverted_index":{"Goal":[0],"inference":[1,22,111,199,220],"is":[2,57,99],"of":[3,9,29,42,138,166,174,208,218],"great":[4],"importance":[5],"for":[6,109],"a":[7,37,83,105,127,145,176,188,204],"variety":[8],"applications":[10],"that":[11,194],"involve":[12,67],"interaction,":[13],"coordination,":[14],"and/or":[15],"competition":[16],"with":[17,59,96],"goal-oriented":[18,189],"agents.":[19],"Typical":[20],"goal":[21,110,198,219],"approaches":[23],"use":[24],"as":[25,33],"many":[26],"pointwise":[27],"measurements":[28,48,91,143,209,223],"the":[30,43,87,93,136,151,162,182,213],"agent's":[31],"trajectory":[32],"possible":[34],"to":[35,134,149,196],"pursue":[36],"most":[38],"accurate":[39],"a-posteriori":[40],"estimate":[41],"goal.":[44],"However,":[45],"taking":[46,222],"frequent":[47],"may":[49,66,73],"not":[50,211],"be":[51,132],"preferred":[52],"in":[53,77,187],"situations":[54],"where":[55,191],"sensing":[56,63,72,97,107,157,185],"associated":[58,95],"high":[60,68,163],"cost":[61,70,94],"(e.g.,":[62],"+":[64],"perception":[65],"computational/bandwidth":[69],"and":[71,92,118,147,156,160,216],"raise":[74],"security":[75],"concerns":[76],"privacy-critical/data-sensitive":[78],"applications).":[79],"In":[80],"such":[81,175],"situations,":[82],"sensible":[84],"tradeoff":[85,152],"between":[86,153],"information":[88,139,154],"gained":[89,140],"from":[90,141],"actions":[98],"highly":[100],"desirable.":[101],"This":[102],"paper":[103],"introduces":[104],"cost-effective":[106,184],"strategy":[108],"tasks":[112],"based":[113],"on":[114],"hybrid":[115,167],"Kalman":[116,168],"filtering":[117,169],"model":[119,128,177],"predictive":[120,129,178],"control.":[121],"Our":[122],"key":[123],"insights":[124],"include:":[125],"1)":[126],"approach":[130,186,202],"can":[131,170],"used":[133],"predict":[135],"amount":[137],"new":[142],"over":[144],"horizon":[146],"thus":[148],"optimize":[150],"gain":[155],"action":[158],"cost,":[159],"2)":[161],"computational":[164],"efficiency":[165],"ensure":[171],"real-time":[172],"feasibility":[173],"approach.":[179],"We":[180],"evaluate":[181],"proposed":[183],"task,":[190],"we":[192],"show":[193],"compared":[195],"standard":[197],"approaches,":[200],"our":[201],"takes":[203],"considerably":[205],"reduced":[206],"number":[207],"while":[210],"impairing":[212],"speed,":[214],"accuracy,":[215],"reliability":[217],"by":[221],"smartly.":[224]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}