{"id":"https://openalex.org/W7166563656","doi":"https://doi.org/10.48550/arxiv.2606.28196","title":"Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space","display_name":"Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166563656","doi":"https://doi.org/10.48550/arxiv.2606.28196"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28196","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28196","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.28196","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027286970","display_name":"Ha Thang Long Doan","orcid":"https://orcid.org/0009-0005-1481-5256"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doan, Ha Thang Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061020240","display_name":"Hikaru Arita","orcid":"https://orcid.org/0000-0003-4953-2553"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arita, Hikaru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101736518","display_name":"Kazuto Nakashima","orcid":"https://orcid.org/0000-0002-6773-7811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nakashima, Kazuto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010975613","display_name":"Kenji Tahara","orcid":"https://orcid.org/0000-0003-4457-7867"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tahara, Kenji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.8309000134468079,"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"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.8309000134468079,"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"}},{"id":"https://openalex.org/T10982","display_name":"Motor Control and Adaptation","score":0.15539999306201935,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.002899999963119626,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6015999913215637},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.576200008392334},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5633999705314636},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.5378999710083008},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5317999720573425},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.5234000086784363},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.46889999508857727}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6015999913215637},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.576200008392334},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5633999705314636},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5303999781608582},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48339998722076416},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C183759332","wikidata":"https://www.wikidata.org/wiki/Q343680","display_name":"Action learning","level":4,"score":0.3912000060081482},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C47932503","wikidata":"https://www.wikidata.org/wiki/Q5395689","display_name":"Error-driven learning","level":3,"score":0.36410000920295715},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3605000078678131},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C12298181","wikidata":"https://www.wikidata.org/wiki/Q7246814","display_name":"Proactive learning","level":5,"score":0.26739999651908875},{"id":"https://openalex.org/C188888258","wikidata":"https://www.wikidata.org/wiki/Q7353390","display_name":"Robot learning","level":4,"score":0.26109999418258667},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28196","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28196","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.28196","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28196","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6813384294509888}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Traditionally,":[0],"dexterous":[1,64],"manipulation":[2,60],"controllers":[3],"are":[4,32],"designed":[5],"using":[6],"analytic":[7],"models":[8],"constrained":[9],"by":[10,62],"strong":[11],"assumptions":[12],"about":[13],"the":[14,17],"hand":[15],"and":[16,46,58,78,83,101,108],"objects":[18],"being":[19],"manipulated.":[20],"Reinforcement":[21],"learning":[22,49,105],"(RL)":[23],"has":[24],"become":[25],"another":[26],"common":[27],"approach":[28],"in":[29,35,48],"which":[30],"skills":[31,61,65],"explored":[33],"openly":[34],"an":[36],"end-to-end":[37],"manner":[38],"but":[39],"is":[40,73],"inefficient":[41],"because":[42],"of":[43],"unnoticeable":[44],"instability":[45],"conflicts":[47],"objectives.":[50],"This":[51],"paper":[52],"attempts":[53],"to":[54],"efficiently":[55],"explore":[56],"stable":[57,91,109],"accurate":[59],"decomposing":[63],"into":[66],"multiple":[67],"simpler/analyzable":[68],"components.":[69],"Each":[70],"skill":[71,104],"component":[72],"subsequently":[74],"learned":[75],"with":[76,95,110],"constraints":[77],"guidance":[79],"from":[80,113],"classical":[81],"physics":[82],"control":[84],"theory.":[85,114],"Our":[86],"work":[87],"shows":[88],"that":[89],"for":[90],"grasp,":[92],"in-grasp":[93],"reposition/reorientation":[94],"different":[96],"objects,":[97],"sensor/motor":[98],"noise,":[99],"latency,":[100],"frictional":[102],"conditions,":[103],"becomes":[106],"efficient":[107],"prior":[111],"knowledge":[112]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-30T00:00:00"}
