{"id":"https://openalex.org/W2782725118","doi":"https://doi.org/10.1109/humanoids.2017.8239531","title":"Humanoid navigation in uneven terrain using learned estimates of traversability","display_name":"Humanoid navigation in uneven terrain using learned estimates of traversability","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2782725118","doi":"https://doi.org/10.1109/humanoids.2017.8239531","mag":"2782725118"},"language":"en","primary_location":{"id":"doi:10.1109/humanoids.2017.8239531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/humanoids.2017.8239531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","raw_type":"proceedings-article"},"type":"article","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/A5075050774","display_name":"Yu-Chi Lin","orcid":"https://orcid.org/0000-0001-5259-534X"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu-Chi Lin","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, U.S.A"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, U.S.A","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083082888","display_name":"Dmitry Berenson","orcid":"https://orcid.org/0000-0002-9712-109X"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitry Berenson","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, U.S.A"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, U.S.A","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075050774"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":0.7282,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.81293419,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"9","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9984999895095825,"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/T10879","display_name":"Robotic Locomotion and Control","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10653","display_name":"Robot Manipulation and Learning","score":0.9925000071525574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.8230901956558228},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.7435517311096191},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.7347199320793152},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6471397280693054},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5623504519462585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5567992329597473},{"id":"https://openalex.org/keywords/humanoid-robot","display_name":"Humanoid robot","score":0.5231172442436218},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5013365745544434},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.45824262499809265},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4325338304042816},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34491974115371704},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09712767601013184},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07913771271705627}],"concepts":[{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.8230901956558228},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.7435517311096191},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.7347199320793152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6471397280693054},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5623504519462585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5567992329597473},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.5231172442436218},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5013365745544434},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.45824262499809265},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4325338304042816},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34491974115371704},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09712767601013184},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07913771271705627},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/humanoids.2017.8239531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/humanoids.2017.8239531","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7300000190734863,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320337392","display_name":"Division of Electrical, Communications and Cyber Systems","ror":"https://ror.org/01krpsy48"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W46565623","https://openalex.org/W104748494","https://openalex.org/W1508300292","https://openalex.org/W1525595943","https://openalex.org/W1588834047","https://openalex.org/W1591987192","https://openalex.org/W1962169445","https://openalex.org/W2008977061","https://openalex.org/W2015149365","https://openalex.org/W2042870409","https://openalex.org/W2084959384","https://openalex.org/W2127895608","https://openalex.org/W2135645359","https://openalex.org/W2161076907","https://openalex.org/W2166870098","https://openalex.org/W2283655042","https://openalex.org/W2536431081","https://openalex.org/W2540579400","https://openalex.org/W2587742116","https://openalex.org/W2738731516","https://openalex.org/W4246876444","https://openalex.org/W4253729359","https://openalex.org/W4295846875","https://openalex.org/W6630414479","https://openalex.org/W6635191027","https://openalex.org/W6678790860","https://openalex.org/W6733045190"],"related_works":["https://openalex.org/W2377402383","https://openalex.org/W2380835401","https://openalex.org/W2381912691","https://openalex.org/W2350381577","https://openalex.org/W2353618196","https://openalex.org/W2348074676","https://openalex.org/W2385033175","https://openalex.org/W2374043190","https://openalex.org/W2363298784","https://openalex.org/W2367402697"],"abstract_inverted_index":{"In":[0,110],"this":[1,111],"paper":[2,112],"we":[3,47,90,113],"explore":[4],"discrete":[5],"search-based":[6],"contact":[7,17,40,63,68,87,106,160],"space":[8,64,88,161],"planning":[9,166,174],"for":[10,34,122,159],"humanoids":[11],"using":[12,150],"both":[13,153],"palm":[14],"and":[15,27,103,115,129,140,155,171],"foot":[16],"in":[18,42,182],"complex":[19],"unstructured":[20],"environments.":[21],"With":[22],"a":[23,39,51,57,72,77],"high":[24],"branching":[25],"factor":[26],"sparse":[28],"contactable":[29,101],"regions,":[30,102],"it":[31],"is":[32],"challenging":[33],"the":[35,62,82,86,93,97,147,156,165,180],"planner":[36,65,94],"to":[37,49,70,95,179],"find":[38,105],"sequence":[41],"such":[43],"environments":[44,184],"quickly.":[45],"Therefore,":[46],"propose":[48,114],"learn":[50],"function":[52,84],"which":[53,134],"predicts":[54],"traversability":[55,79],"-":[56],"measure":[58],"of":[59,85,119],"how":[60],"quickly":[61],"can":[66,91],"generate":[67],"sequences":[69,107],"traverse":[71],"certain":[73],"region.":[74],"By":[75],"including":[76],"learned":[78],"estimate":[80],"into":[81],"heuristic":[83,158],"planner,":[89],"bias":[92],"search":[96],"areas":[98],"with":[99,185],"more":[100,108],"thus":[104],"efficiently.":[109],"evaluate":[116],"two":[117],"kinds":[118],"feature":[120],"vectors":[121],"estimating":[123],"traversability:":[124],"Exact":[125],"Contact":[126,131],"Checking":[127,132],"(ECC)":[128],"Approximate":[130],"(ACC),":[133],"make":[135],"different":[136],"trade-offs":[137],"between":[138],"speed":[139],"accuracy.":[141],"The":[142],"experimental":[143],"results":[144],"show":[145],"that":[146],"proposed":[148],"approach":[149],"ACC":[151,163],"outperforms":[152],"ECC":[154],"baseline":[157,181],"planning;":[162],"increases":[164],"success":[167],"rate":[168],"by":[169,176],"19%":[170],"reduces":[172],"average":[173],"time":[175],"24%":[177],"compared":[178],"difficult":[183],"uneven":[186],"terrain.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}