{"id":"https://openalex.org/W7160333587","doi":"https://doi.org/10.48550/arxiv.2605.02192","title":"Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation","display_name":"Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160333587","doi":"https://doi.org/10.48550/arxiv.2605.02192"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02192","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02192","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135380180","display_name":"Shanze Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shanze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135391522","display_name":"Xinming Zhang","orcid":"https://orcid.org/0000-0003-4271-0898"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xinming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134796295","display_name":"Siwei Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Siwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135303933","display_name":"Xianghui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xianghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135390196","display_name":"Hailong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Changwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135291107","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0002-8170-870X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Hailong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Zhang, Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.30239999294281006,"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":0.30239999294281006,"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":0.19629999995231628,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.1340000033378601,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/reset","display_name":"Reset (finance)","score":0.8263999819755554},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.7616999745368958},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.746399998664856},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6473000049591064},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.6104999780654907},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5752000212669373},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.566100001335144},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5530999898910522}],"concepts":[{"id":"https://openalex.org/C2779795794","wikidata":"https://www.wikidata.org/wiki/Q7315343","display_name":"Reset (finance)","level":2,"score":0.8263999819755554},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.7616999745368958},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.746399998664856},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6473000049591064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6377000212669373},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6104999780654907},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5752000212669373},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.566100001335144},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5530999898910522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40209999680519104},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3953999876976013},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3352999985218048},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.29339998960494995},{"id":"https://openalex.org/C74222875","wikidata":"https://www.wikidata.org/wiki/Q16000312","display_name":"Robot kinematics","level":4,"score":0.29010000824928284},{"id":"https://openalex.org/C199668693","wikidata":"https://www.wikidata.org/wiki/Q1550329","display_name":"Collision detection","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C150415221","wikidata":"https://www.wikidata.org/wiki/Q40687","display_name":"Robotic arm","level":2,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02192","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":"doi:10.48550/arxiv.2605.02192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02192","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":false,"raw_source_name":null,"raw_type":"article"},"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":{"Should":[0],"a":[1,29,37,42,82],"single":[2],"collision":[3,26,43,91,127],"necessarily":[4],"terminate":[5],"an":[6],"entire":[7],"navigation":[8],"episode?":[9],"In":[10,73],"most":[11,131],"deep":[12],"reinforcement":[13],"learning":[14,66,115],"(DRL)":[15],"frameworks":[16],"for":[17],"robot":[18,138],"navigation,":[19],"this":[20,74,78],"remains":[21],"the":[22,51,57,69,98,105,130,142,145],"standard":[23],"practice:":[24],"every":[25],"immediately":[27],"triggers":[28],"global":[30,94],"environment":[31,95],"reset":[32,84],"and":[33,80],"is":[34],"penalized":[35],"as":[36],"complete":[38],"task":[39,48],"failure.":[40],"While":[41],"during":[44,54],"deployment":[45],"naturally":[46],"indicates":[47],"failure,":[49],"applying":[50],"same":[52,106],"treatment":[53],"training":[55,71],"prevents":[56],"agent":[58,99],"from":[59,93],"exploring":[60],"challenging":[61],"obstacle":[62],"configurations,":[63],"which":[64],"slows":[65],"progress":[67],"in":[68,148],"early":[70],"phase.":[72],"work,":[75],"we":[76],"challenge":[77],"convention":[79],"propose":[81],"Multi-Collision":[83],"Budget":[85],"(MCB)":[86],"framework":[87],"that":[88,111],"decouples":[89],"local":[90],"termination":[92],"resets,":[96],"allowing":[97],"to":[100],"retry":[101],"difficult":[102],"configurations":[103],"within":[104],"episode.":[107],"Simulation":[108],"experiments":[109,135],"show":[110],"MCB":[112],"improves":[113],"early-stage":[114],"efficiency":[116],"by":[117],"reaching":[118],"target":[119],"success-rate":[120],"levels":[121],"with":[122,125],"fewer":[123],"interactions,":[124],"small":[126],"budgets":[128],"producing":[129],"consistent":[132],"gains.":[133],"Real-world":[134],"on":[136],"heterogeneous":[137],"platforms":[139],"further":[140],"validate":[141],"deployability":[143],"of":[144],"learned":[146],"policies":[147],"cluttered":[149],"environments.":[150]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}
