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MOREOVER  EVEN VERY SOPHISTICATED TACTILE SENSORS SOMETIMES PRODUCE VERY NOISY AND ATTENUATED DATA.THIS PROPOSAL IS TO MAKE FLEXIBLE MATERIALS MANIPULATION MORE PRACTICAL BY DEVELOPING NEW METHODS OF CREATING AND USING HAPTIC MODELS. WE PROPOSE TO LEVERAGE EXISTING ROBOT LOCALIZATION AND MAPPING WORK DEVELOPED IN THE MOBILE ROBOT COMMUNITY. ALREADY IN OUR LAB  WE HAVE STARTED THIS WORK AND DEVELOPED AND DEMONSTRATED SOME BASIC TACTILE MAPPING TECHNIQUES THAT WERE SUMMARIZED AT THE 2013 NASA NRI PI MEETING. HERE  WE PROPOSE TO APPLY THIS WORK IN REAL ROBOT MANIPULATION CONTEXTS  CULMINATING IN A DEMONSTRATION OF TACTILE MAPPING AND LOCALIZATION CAPABILITIES IN THE CONTEXT OF SMALL AND FLEXIBLE MATERIALS MANIPULATION TASKS. IN YEAR 1  WE PROPOSE TO CONTINUE TO EXPLORE TACTILE REGISTRATION AND MAPPING TECHNIQUES AND TO DEMONSTRATE BASIC TACTILELOCALIZATION CAPABILITIES IN THE CONTEXT OF A FINE-MATERIALS INSERTION TASK. WE EXPECT THIS WORK TO BE DEMONSTRATED IN THE CONTEXT OF AN EXPERIMENT WITH THE BAXTER ROBOT  LOCATED AT NORTHEASTERN UNIVERSITY. IN YEAR 2  WE PROPOSE TO EXPAND THESE MANIPULATION CAPABILITIES BY DEVELOPING MORE ROBUST AND SOPHISTICATED PLANNING AND CONTROL METHODS THAT REASON ABOUT HOW THE MANIPULATION STRATEGY AFFECTS THE INFORMATION CONTENT AND SAFETY ENVELOPE OF THE MANIPULATION TASK.","funder_award_id":"NNX13AQ85G","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"},{"id":"https://openalex.org/G6779932216","display_name":"NRI: Collaborative Research: Human-Supervised Perception and Grasping in  Clutter","funder_award_id":"1427081","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2290564286.pdf","grobid_xml":"https://content.openalex.org/works/W2290564286.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W46565623","https://openalex.org/W1503925285","https://openalex.org/W1564897360","https://openalex.org/W1625949922","https://openalex.org/W1892339738","https://openalex.org/W1966747088","https://openalex.org/W1978131245","https://openalex.org/W1999156278","https://openalex.org/W2005756025","https://openalex.org/W2021683594","https://openalex.org/W2076398395","https://openalex.org/W2112796928","https://openalex.org/W2155893237","https://openalex.org/W2165308133","https://openalex.org/W2201912979","https://openalex.org/W2293883387","https://openalex.org/W2300618187","https://openalex.org/W2417956634","https://openalex.org/W2736534894","https://openalex.org/W3217246742","https://openalex.org/W6687808982","https://openalex.org/W6697619730"],"related_works":["https://openalex.org/W1999156278","https://openalex.org/W1892339738","https://openalex.org/W2963654160","https://openalex.org/W2201912979","https://openalex.org/W2041376653","https://openalex.org/W2953249127","https://openalex.org/W1503925285","https://openalex.org/W2962736495","https://openalex.org/W2300618187","https://openalex.org/W2922340928","https://openalex.org/W2964161785","https://openalex.org/W2962737955","https://openalex.org/W2949379496","https://openalex.org/W2414685554","https://openalex.org/W2194775991","https://openalex.org/W3091410168","https://openalex.org/W2910688493","https://openalex.org/W3080749190","https://openalex.org/W3094134252","https://openalex.org/W3160245612"],"abstract_inverted_index":{"This":[0,152],"paper":[1,45],"considers":[2],"the":[3,49,78,92,99],"problem":[4],"of":[5,24,32,72,80,84,91,147],"grasp":[6,26,73,117,129,144],"pose":[7],"detection":[8],"in":[9,43,149],"point":[10],"clouds.":[11],"We":[12,67,131],"follow":[13],"a":[14,21,35,38,63,114,136,154],"general":[15],"algorithmic":[16],"structure":[17],"that":[18,113],"first":[19],"generates":[20],"large":[22,58],"set":[23],"6-DOF":[25],"candidates":[27],"and":[28,75,97,124,140],"then":[29],"classifies":[30],"each":[31],"them":[33],"as":[34,120,122],"good":[36],"or":[37,88],"bad":[39],"grasp.":[40],"Our":[41,110],"focus":[42],"this":[44],"is":[46,153],"on":[47,101,135],"improving":[48],"second":[50],"step":[51],"by":[52],"using":[53,81],"depth":[54,103],"sensor":[55],"scans":[56],"from":[57,106],"online":[59],"datasets":[60],"to":[61,94,158],"train":[62],"convolutional":[64],"neural":[65],"network.":[66],"propose":[68],"two":[69,85],"new":[70],"representations":[71],"candidates,":[74],"we":[76],"quantify":[77],"effect":[79],"prior":[82,125,160],"knowledge":[83,90,126],"forms:":[86],"instance":[87],"category":[89],"object":[93],"be":[95],"grasped,":[96],"pretraining":[98,123],"network":[100],"simulated":[102],"data":[104],"obtained":[105],"idealized":[107],"CAD":[108],"models.":[109],"analysis":[111],"shows":[112],"more":[115],"informative":[116],"candidate":[118],"representation":[119],"well":[121],"significantly":[127],"improve":[128],"detection.":[130],"evaluate":[132],"our":[133,159],"approach":[134],"Baxter":[137],"Research":[138],"Robot":[139],"demonstrate":[141],"an":[142],"average":[143],"success":[145],"rate":[146],"93%":[148],"dense":[150],"clutter.":[151],"20%":[155],"improvement":[156],"compared":[157],"work.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":4}],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2025-10-10T00:00:00"}