{"id":"https://openalex.org/W3133935485","doi":"https://doi.org/10.1109/icra48506.2021.9560787","title":"Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning","display_name":"Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning","publication_year":2021,"publication_date":"2021-05-30","ids":{"openalex":"https://openalex.org/W3133935485","doi":"https://doi.org/10.1109/icra48506.2021.9560787","mag":"3133935485"},"language":"en","primary_location":{"id":"doi:10.1109/icra48506.2021.9560787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9560787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.04727","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015381297","display_name":"Patrick Wenzel","orcid":"https://orcid.org/0000-0002-1846-6701"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Patrick Wenzel","raw_affiliation_strings":["Technical University of Munich, Germany","Technical University of Munich, Germany#TAB#"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Germany","institution_ids":["https://openalex.org/I62916508"]},{"raw_affiliation_string":"Technical University of Munich, Germany#TAB#","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078954824","display_name":"Torsten Sch\u00f6n","orcid":"https://orcid.org/0000-0001-5763-3392"},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Torsten Schon","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Ingolstadt, Germany","Technische Hochschule Ingolstadt,Ingolstadt,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]},{"raw_affiliation_string":"Technische Hochschule Ingolstadt,Ingolstadt,Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090283032","display_name":"Laura Leal-Taix\u00e9","orcid":"https://orcid.org/0000-0001-8709-1133"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Laura Leal-Taixe","raw_affiliation_strings":["Technical University of Munich, Germany","Technical University of Munich, Germany#TAB#"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Germany","institution_ids":["https://openalex.org/I62916508"]},{"raw_affiliation_string":"Technical University of Munich, Germany#TAB#","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087710605","display_name":"Daniel Cremers","orcid":"https://orcid.org/0000-0002-3079-7984"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Cremers","raw_affiliation_strings":["Technical University of Munich, Germany","Technical University of Munich, Germany#TAB#"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Germany","institution_ids":["https://openalex.org/I62916508"]},{"raw_affiliation_string":"Technical University of Munich, Germany#TAB#","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5015381297"],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":null,"apc_paid":null,"fwci":0.6999,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.75166325,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"14360","last_page":"14366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9994999766349792,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9994999766349792,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9994000196456909,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.8947118520736694},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7713712453842163},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7686847448348999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.712153971195221},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.701694905757904},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.6073643565177917},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.595507025718689},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.5740678906440735},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.4781726002693176},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.46590501070022583},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4628002345561981},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4339030086994171},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4288315176963806},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4164563715457916},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.08422455191612244}],"concepts":[{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.8947118520736694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7713712453842163},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7686847448348999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.712153971195221},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.701694905757904},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.6073643565177917},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.595507025718689},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.5740678906440735},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.4781726002693176},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46590501070022583},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4628002345561981},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4339030086994171},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4288315176963806},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4164563715457916},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.08422455191612244},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icra48506.2021.9560787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9560787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.04727","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.04727","pdf_url":"https://arxiv.org/pdf/2103.04727","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"},{"id":"mag:3133935485","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2103.04727","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2103.04727","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.04727","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.04727","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.04727","pdf_url":"https://arxiv.org/pdf/2103.04727","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3133935485.pdf","grobid_xml":"https://content.openalex.org/works/W3133935485.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W612478963","https://openalex.org/W1612997784","https://openalex.org/W1973859698","https://openalex.org/W2038794597","https://openalex.org/W2145339207","https://openalex.org/W2155968351","https://openalex.org/W2167340365","https://openalex.org/W2173564293","https://openalex.org/W2201581102","https://openalex.org/W2257979135","https://openalex.org/W2434014514","https://openalex.org/W2474281075","https://openalex.org/W2513734981","https://openalex.org/W2528501250","https://openalex.org/W2547373914","https://openalex.org/W2567015638","https://openalex.org/W2583598337","https://openalex.org/W2592873849","https://openalex.org/W2736601468","https://openalex.org/W2746553466","https://openalex.org/W2761873684","https://openalex.org/W2795910581","https://openalex.org/W2809461852","https://openalex.org/W2963073614","https://openalex.org/W2963428623","https://openalex.org/W2964121744","https://openalex.org/W3103648783","https://openalex.org/W6618872416","https://openalex.org/W6631190155","https://openalex.org/W6685444567","https://openalex.org/W6687681856","https://openalex.org/W6718092244","https://openalex.org/W6725867758","https://openalex.org/W6727899713","https://openalex.org/W6728854336","https://openalex.org/W6731334075","https://openalex.org/W6732656399","https://openalex.org/W6741002519","https://openalex.org/W6745347688","https://openalex.org/W6752356361","https://openalex.org/W6756486208","https://openalex.org/W6780559895"],"related_works":["https://openalex.org/W3205553845","https://openalex.org/W3095452501","https://openalex.org/W3081066269","https://openalex.org/W3194398395","https://openalex.org/W3011184007","https://openalex.org/W2076745239","https://openalex.org/W1591260266","https://openalex.org/W2914898814","https://openalex.org/W3000542802","https://openalex.org/W2973989257","https://openalex.org/W3045396729","https://openalex.org/W2891884329","https://openalex.org/W3203757447","https://openalex.org/W2567020712","https://openalex.org/W3160967954","https://openalex.org/W3085271681","https://openalex.org/W3116833835","https://openalex.org/W2798472928","https://openalex.org/W2969277962","https://openalex.org/W2892253383"],"abstract_inverted_index":{"Obstacle":[0],"avoidance":[1,23,64,97],"is":[2],"a":[3,36,100,160],"fundamental":[4],"and":[5,72,86,113,147],"challenging":[6],"problem":[7,20,48,98],"for":[8],"autonomous":[9],"navigation":[10],"of":[11,21,58,79,132,151],"mobile":[12],"robots.":[13],"In":[14,40],"this":[15,47],"paper,":[16],"we":[17,42,93,140],"consider":[18,62],"the":[19,29,59,76,95,145,149,152],"obstacle":[22,63,70,96],"in":[24,45,83,91,130,136],"simple":[25],"3D":[26],"environments":[27],"where":[28],"robot":[30],"has":[31],"to":[32,143],"solely":[33],"rely":[34],"on":[35,51],"single":[37],"monocular":[38],"camera.":[39],"particular,":[41],"are":[43,125],"interested":[44],"solving":[46],"without":[49],"relying":[50],"localization,":[52],"mapping,":[53],"or":[54],"planning":[55],"techniques.":[56],"Most":[57],"existing":[60],"work":[61],"as":[65,99,111,117],"two":[66],"separate":[67],"problems,":[68],"namely":[69],"detection,":[71],"control.":[73],"Inspired":[74],"by":[75,154,159],"recent":[77],"advantages":[78],"deep":[80,103],"reinforcement":[81],"learning":[82,104,146],"Atari":[84],"games":[85],"understanding":[87],"highly":[88],"complex":[89],"situations":[90],"Go,":[92],"tackle":[94],"data-driven":[101],"end-to-end":[102],"approach.":[105],"Our":[106],"approach":[107],"takes":[108],"raw":[109],"images":[110],"input":[112],"generates":[114],"control":[115,128],"commands":[116,129],"output.":[118],"We":[119],"show":[120,141],"that":[121],"discrete":[122],"action":[123],"spaces":[124],"outperforming":[126],"continuous":[127],"terms":[131],"expected":[133],"average":[134],"reward":[135],"maze-like":[137],"environments.":[138],"Furthermore,":[139],"how":[142],"accelerate":[144],"increase":[148],"robustness":[150],"policy":[153],"incorporating":[155],"predicted":[156],"depth":[157],"maps":[158],"generative":[161],"adversarial":[162],"network.":[163]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}