{"data":[{"id":"10.5281/zenodo.19461738","type":"dois","attributes":{"doi":"10.5281/zenodo.19461738","identifiers":[{"identifier":"oai:zenodo.org:19461738","identifierType":"oai"}],"creators":[{"name":"HAMZAH, SEYED RASOUL","nameType":"Personal","givenName":"SEYED RASOUL","familyName":"HAMZAH","nameIdentifiers":[{"nameIdentifier":"0009-0009-3175-8563","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"\"What Lies Ahead is Not the Death of a Civilization, But the Dawn of a New, Tensor-Based Breuerian Civilization Arising from the Old and Highly Respected One—and This is the Logical and Merciless Inevitability of Mathematics. Every Phoenix Rises from the Ashes of Destruction.\""}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"CIA, NSA, DIA, FBI, NRO, NGA, US Cyber Command, Pentagon, US Army, US Navy, US Air Force, US Marine Corps, Coast Guard, Special Forces, Mossad, Shin Bet, Aman, IDF, Israeli Air Force, Israeli Navy, Israeli Intelligence Corps, MI6, MI5, GCHQ, BND, MAD, DGSE, DGSI, NATO, Allied Command Operations, Allied Command Transformation, EU INTCEN, European Defence Agency, Frontex, British Army, French Army, French Navy, French Air Force, Bundeswehr, CSIS, CSE, Defense Intelligence UK, National Security Council, ONI, USCG Intelligence, DARPA, NCTC, Homeland Security, JSOC, STRATCOM, SPACECOM, INSCOM, AF ISR, ESOC, AISE, SKW, MUST, NIS, MIVD, SGRS, FE, CNI, EYP, MIT, SIE, NATO Allied Intelligence Fusion Centre, Allied Maritime Command, Allied Air Command, Joint Force Command Brunssum, Joint Force Command Naples, NATO Special Operations Headquarters, 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Transformation (ACT), EU Intelligence and Situation Centre (EU INTCEN), European Defence Agency (EDA), Frontex, Joint European Union Intelligence School (JEIS), British Army, French Army, French Navy, French Air Force, Bundeswehr, Canadian Security Intelligence Service (CSIS), Communications Security Establishment (CSE), Defence Intelligence (UK), National Security Council (US), Office of Naval Intelligence (ONI), Coast Guard Intelligence (USCG), Defense Advanced Research Projects Agency (DARPA), National Reconnaissance Office (NRO), National Geospatial-Intelligence Agency (NGA), Cyber Command, National Counterterrorism Center (NCTC), Homeland Security, Joint Special Operations Command (JSOC), Special Reconnaissance Unit, Strategic Command (STRATCOM), Space Command (SPACECOM), Defense Intelligence Agency (DIA), National Guard Bureau, U.S. Army Intelligence and Security Command (INSCOM), U.S. Air Force Intelligence, Surveillance and Reconnaissance Agency (AF ISR), European Space 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Every Phoenix Rises From The Ashes Of Destruction.\"\n\n.…………………….\n\nCreator of Hamzah Equation\n\n08.04.2026","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19461738","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-07T19:41:08Z","registered":"2026-04-07T19:41:08Z","published":null,"updated":"2026-04-08T06:44:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19461737","type":"dois","attributes":{"doi":"10.5281/zenodo.19461737","identifiers":[],"creators":[{"name":"HAMZAH, SEYED RASOUL","nameType":"Personal","givenName":"SEYED RASOUL","familyName":"HAMZAH","nameIdentifiers":[{"nameIdentifier":"0009-0009-3175-8563","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"\"What Lies Ahead is Not the Death of a Civilization, But the Dawn of a New, Tensor-Based Breuerian Civilization Arising from the Old and Highly Respected One—and This is the Logical and Merciless Inevitability of Mathematics. Every Phoenix Rises from the Ashes of Destruction.\""}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"CIA, NSA, DIA, FBI, NRO, NGA, US Cyber Command, Pentagon, US Army, US Navy, US Air Force, US Marine Corps, Coast Guard, Special Forces, Mossad, Shin Bet, Aman, IDF, Israeli Air Force, Israeli Navy, Israeli Intelligence Corps, MI6, MI5, GCHQ, BND, MAD, DGSE, DGSI, NATO, Allied Command Operations, Allied Command Transformation, EU INTCEN, European Defence Agency, Frontex, British Army, French Army, French Navy, French Air Force, Bundeswehr, CSIS, CSE, Defense Intelligence UK, National Security Council, ONI, USCG Intelligence, DARPA, NCTC, Homeland Security, JSOC, STRATCOM, SPACECOM, INSCOM, AF ISR, ESOC, AISE, SKW, MUST, NIS, MIVD, SGRS, FE, CNI, EYP, MIT, SIE, NATO Allied Intelligence Fusion Centre, Allied Maritime Command, Allied Air Command, Joint Force Command Brunssum, Joint Force Command Naples, NATO Special Operations Headquarters, 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Intelligence Analysis, Security Studies, National Defense, Military Strategy, Defense Policy, International Security, Military Intelligence, Surveillance, Reconnaissance, Defense Research, Threat Assessment, Strategic Studies, Global Security, Intelligence Cooperation, Defense Alliances, Cyber Defense, Counterintelligence, Intelligence Sharing, Security Policy, Military Education, Intelligence Training, Intelligence Community, Tactical Operations, Strategic Operations, Logistics, Defense Planning, Military Communications, Satellite Intelligence, UAV Operations, Drone Surveillance, Military Exercises, War Games, Peacekeeping, Civil Defense, Homeland Defense, Border Security, Critical Infrastructure Protection, Crisis Management, Emergency Response, Military Doctrine, Defense Budget, Defense Innovation, Weapons Development, Ballistic Missile Defense, Air Defense, Naval Operations, Submarine Warfare, Electronic Countermeasures, Cyber Operations, Cyber Threats, Cybersecurity Policy, Threat 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Every Phoenix Rises From The Ashes Of Destruction.\"\n\n.…………………….\n\nCreator of Hamzah Equation\n\n08.04.2026","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19461737","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-07T19:41:08Z","registered":"2026-04-07T19:41:09Z","published":null,"updated":"2026-04-08T06:44:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.17632/gyg2pnsv6m.1","type":"dois","attributes":{"doi":"10.17632/gyg2pnsv6m.1","identifiers":[],"creators":[{"name":"卢, 磊","nameType":"Personal","givenName":"磊","familyName":"卢","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Trajectory accuracy improving for robot mirror surface machining based on local distance error information"}],"publisher":"Mendeley Data","container":{},"publicationYear":2026,"subjects":[{"subject":"Mechanical Machining"},{"subject":"Compensation Method"},{"subject":"Manufacturing Robotics"}],"contributors":[{"name":"Soochow University","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[{"schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/05t8y2r12","nameIdentifierScheme":"ROR"}]}],"dates":[{"date":"2026-04-08T03:55:58Z","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.17632/gyg2pnsv6m","resourceTypeGeneral":"Dataset","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This research data includes three algorithms designed to enhance robot machining trajectory accuracy by utilizing local error information, along with data for the improvement of mirror surface machining trajectories.\nAlgorithm 1​ calculates the local distance error influence vector for a given trajectory. This vector reflects the local characteristics of the trajectory.\nAlgorithm 2​ filters the measured trajectory points based on the influence vector obtained from Algorithm 1, selecting the error measurement trajectory points that are most meaningful for locally improving the trajectory accuracy.\nAlgorithm 3​ is an iterative compensation algorithm that corrects the trajectory based on the measured local errors.\nAll algorithms were implemented in MATLAB R2024a.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://data.mendeley.com/datasets/gyg2pnsv6m/1","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-08T03:55:59Z","registered":"2026-04-08T03:56:00Z","published":null,"updated":"2026-04-08T03:56:00Z"},"relationships":{"client":{"data":{"id":"bl.mendeley","type":"clients"}}}},{"id":"10.17632/gyg2pnsv6m","type":"dois","attributes":{"doi":"10.17632/gyg2pnsv6m","identifiers":[],"creators":[{"name":"卢, 磊","nameType":"Personal","givenName":"磊","familyName":"卢","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Trajectory accuracy improving for robot mirror surface machining based on local distance error information"}],"publisher":"Mendeley Data","container":{},"publicationYear":2026,"subjects":[{"subject":"Mechanical Machining"},{"subject":"Compensation Method"},{"subject":"Manufacturing Robotics"}],"contributors":[{"name":"Soochow University","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[{"schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/05t8y2r12","nameIdentifierScheme":"ROR"}]}],"dates":[{"date":"2026-04-08T03:55:58Z","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.17632/gyg2pnsv6m.1","resourceTypeGeneral":"Dataset","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This research data includes three algorithms designed to enhance robot machining trajectory accuracy by utilizing local error information, along with data for the improvement of mirror surface machining trajectories.\nAlgorithm 1​ calculates the local distance error influence vector for a given trajectory. This vector reflects the local characteristics of the trajectory.\nAlgorithm 2​ filters the measured trajectory points based on the influence vector obtained from Algorithm 1, selecting the error measurement trajectory points that are most meaningful for locally improving the trajectory accuracy.\nAlgorithm 3​ is an iterative compensation algorithm that corrects the trajectory based on the measured local errors.\nAll algorithms were implemented in MATLAB R2024a.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://data.mendeley.com/datasets/gyg2pnsv6m","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2026-04-08T03:55:58Z","registered":"2026-04-08T03:55:58Z","published":null,"updated":"2026-04-08T03:55:58Z"},"relationships":{"client":{"data":{"id":"bl.mendeley","type":"clients"}}}},{"id":"10.6084/m9.figshare.31956945.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.31956945.v1","identifiers":[],"creators":[{"name":"Liang, Xinan","givenName":"Xinan","familyName":"Liang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Regarding the functional relationship between the proposed household service companion robots and user needs, it mainly includes MATLAB algorithm codes and corresponding datasets."}],"publisher":"figshare","container":{},"publicationYear":2026,"subjects":[{"subject":"Artificial life and complex adaptive systems","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"460201"},{"subject":"Fuzzy computation","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"460204"},{"subject":"Intelligent robotics","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"460205"}],"contributors":[],"dates":[{"date":"2026-04-08","dateType":"Created"},{"date":"2026-04-08","dateType":"Updated"},{"date":"2026-04-08","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.31956945","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1241590 Bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This data reveals the functional relationship between the home service companion robots we studied and user needs. 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It contains heart rate and work rate measurements recorded during robotics-assisted tilt table exercise.\nThese datasets are associated with the article: L. Brockmann, J. Saengsuwan, C. Schuster-Amft and K. J. Hunt, \"Feedback control of heart rate during robotics-assisted tilt table exercise in patients after stroke: a clinical feasibility study\", J. Neuroeng. Rehabil., 2023, Submitted.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://olos.swiss/portal//archives/32e326d0-a7c4-4730-9e45-424693773825","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-12-01T04:30:02Z","registered":"2023-12-01T04:30:03Z","published":null,"updated":"2026-04-08T03:30:06Z"},"relationships":{"client":{"data":{"id":"azob.olos","type":"clients"}}}},{"id":"10.34914/olos:2zknihrcvbg6heuq3e4ipoh76q","type":"dois","attributes":{"doi":"10.34914/olos:2zknihrcvbg6heuq3e4ipoh76q","identifiers":[],"creators":[{"name":"Brockmann, Lars","nameType":"Personal","affiliation":[],"nameIdentifiers":[]},{"name":"Hunt, Kenneth James","nameType":"Personal","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Changes in heart rate variability at rest and during exercise in patients after stroke: a preliminary study"}],"publisher":"OLOS.swiss, OLOS","container":{},"publicationYear":2023,"subjects":[{"subject":"Cardiopulmonary fitness;Exercise training;Exercise testing;Peak oxygen uptake;Robotics;Robotics-assisted tilt table;Stroke"}],"contributors":[{"name":"\n    [ResearchGroup]=[7809b3ff-ed61-439b-9f94-ed0f5cc0305b] BFH - Institute for Human Centered Engineering (HUCE) (Organizational)\n   ","contributorType":"Other","affiliation":[],"nameIdentifiers":[]},{"name":"\n    [ProjectManager]=Stalder, Désirée (Personal)\n   ","contributorType":"Other","affiliation":[],"nameIdentifiers":[]},{"name":"\n    [ProjectManager]=Pfyffer, Samuel (Personal)\n   ","contributorType":"Other","affiliation":[],"nameIdentifiers":[]},{"name":"\n    [ProjectManager]=Scheuner, Marc (Personal)\n   ","contributorType":"Other","affiliation":[],"nameIdentifiers":[]},{"name":"\n    [ProjectManager]=Lüdi, Chantal (Personal)\n   ","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2023","dateType":"Available"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["application/json","text/plain","text/xml"],"version":null,"rightsList":[],"descriptions":[{"description":"[Abstract]=These datasets are associated with the article: J. Saengsuwan, L. Brockmann, C. Schuster-Amft and K. J. Hunt, \"Changes in heart rate variability at rest and during exercise in patients after stroke: a preliminary study\". It contains heart rate and work rate measurements recorded during robotics-assisted tilt table exercise from 12 patients with neurological impairments secondary to stroke.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://olos.swiss/portal//archives/6eb8a716-d5fc-4642-9262-bd52a73d4ec5","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-01-10T04:30:02Z","registered":"2024-01-10T04:30:02Z","published":null,"updated":"2026-04-08T03:30:06Z"},"relationships":{"client":{"data":{"id":"azob.olos","type":"clients"}}}},{"id":"10.48550/arxiv.2604.03386","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.03386","identifiers":[{"identifier":"2604.03386","identifierType":"arXiv"}],"creators":[{"name":"Medvid, Sergii","nameType":"Personal","givenName":"Sergii","familyName":"Medvid","affiliation":[],"nameIdentifiers":[]},{"name":"Valenia, Andrii","nameType":"Personal","givenName":"Andrii","familyName":"Valenia","affiliation":[],"nameIdentifiers":[]},{"name":"Glybovets, Mykola","nameType":"Personal","givenName":"Mykola","familyName":"Glybovets","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Activity-Dependent Plasticity in Morphogenetically-Grown Recurrent Networks"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"lang":"en","subject":"Neural and Evolutionary Computing (cs.NE)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-04-03T18:35:13Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-08T00:24:54Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Developmental approaches to neural architecture search grow functional networks from compact genomes through self-organisation, but the resulting networks operate with fixed post-growth weights. We characterise Hebbian and anti-Hebbian plasticity across 50,000 morphogenetically grown recurrent controllers (5M+ configurations on CartPole and Acrobot), then test whether co-evolutionary experiments -- where plasticity parameters are encoded in the genome and evolved alongside the developmental architecture -- recover these patterns independently. Our characterisation reveals that (1) anti-Hebbian plasticity significantly outperforms Hebbian for competent networks (Cohen's d = 0.53-0.64), (2) regret (fraction of oracle improvement lost under the best fixed setting) reaches 52-100%, and (3) plasticity's role shifts from fine-tuning to genuine adaptation under non-stationarity. Co-evolution independently discovers these patterns: on CartPole, 70% of runs evolve anti-Hebbian plasticity (p = 0.043); on Acrobot, evolution finds near-zero eta with mixed signs -- exactly matching the characterisation. A random-RNN control shows that anti-Hebbian dominance is generic to small recurrent networks, but the degree of topology-dependence is developmental-specific: regret is 2-6x higher for morphogenetically grown networks than for random graphs with matched topology statistics.","descriptionType":"Abstract"},{"description":"7 pages, 6 figures","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.03386","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-07T02:40:09Z","registered":"2026-04-07T02:40:10Z","published":null,"updated":"2026-04-08T02:37:42Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2604.02779","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.02779","identifiers":[{"identifier":"2604.02779","identifierType":"arXiv"}],"creators":[{"name":"Zhang, Linzuo","nameType":"Personal","givenName":"Linzuo","familyName":"Zhang","affiliation":[],"nameIdentifiers":[]},{"name":"Hu, Yu","nameType":"Personal","givenName":"Yu","familyName":"Hu","affiliation":[],"nameIdentifiers":[]},{"name":"Yu, Feng","nameType":"Personal","givenName":"Feng","familyName":"Yu","affiliation":[],"nameIdentifiers":[]},{"name":"Deng, Yang","nameType":"Personal","givenName":"Yang","familyName":"Deng","affiliation":[],"nameIdentifiers":[]},{"name":"Yu, Wenxian","nameType":"Personal","givenName":"Wenxian","familyName":"Yu","affiliation":[],"nameIdentifiers":[]},{"name":"Zou, Danping","nameType":"Personal","givenName":"Danping","familyName":"Zou","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-04-03T06:44:10Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-06T00:26:33Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T15:16:17Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T01:04:03Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"-Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches often assume regularly shaped gaps, leading to poor generalization and limited practicality. In this work, we present a fully vision-based, end-to-end framework that maps depth images directly to control commands, enabling drones to traverse complex gaps within unseen environments. Operating in the Special Euclidean group SE(3), where position and orientation are tightly coupled, the framework leverages differentiable simulation, a Stop-Gradient operator, and a Bimodal Initialization Distribution to achieve stable traversal through consecutive gaps. Two auxiliary prediction modules-a gap-crossing success classifier and a traversability predictor-further enhance continuous navigation and safety. Extensive simulation and real-world experiments demonstrate the approach's effectiveness, generalization capability, and practical robustness.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.02779","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-06T01:34:19Z","registered":"2026-04-06T01:34:20Z","published":null,"updated":"2026-04-08T02:37:33Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2604.01346","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.01346","identifiers":[{"identifier":"2604.01346","identifierType":"arXiv"}],"creators":[{"name":"Parmar, Manoj","nameType":"Personal","givenName":"Manoj","familyName":"Parmar","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Safety, Security, and Cognitive Risks in World Models"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Cryptography and Security (cs.CR)","subjectScheme":"arXiv"},{"lang":"en","subject":"Artificial Intelligence (cs.AI)","subjectScheme":"arXiv"},{"lang":"en","subject":"Machine Learning (cs.LG)","subjectScheme":"arXiv"},{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"lang":"en","subject":"I.2.6; I.2.8; K.6.5","subjectScheme":"ACM"},{"lang":"en","subject":"68M25, 68T05, 68T07, 68T40","subjectScheme":"MSC"}],"contributors":[],"dates":[{"date":"2026-04-01T19:57:33Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-03T00:05:13Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-06T19:07:02Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:04:53Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution Non Commercial No Derivatives 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-nd-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"World models - learned internal simulators of environment dynamics - are rapidly becoming foundational to autonomous decision-making in robotics, autonomous vehicles, and agentic AI. By predicting future states in compressed latent spaces, they enable sample-efficient planning and long-horizon imagination without direct environment interaction. Yet this predictive power introduces a distinctive set of safety, security, and cognitive risks. Adversaries can corrupt training data, poison latent representations, and exploit compounding rollout errors to cause significant degradation in safety-critical deployments. At the alignment layer, world model-equipped agents are more capable of goal misgeneralisation, deceptive alignment, and reward hacking. At the human layer, authoritative world model predictions foster automation bias, miscalibrated trust, and planning hallucination.\n This paper surveys the world model landscape; introduces formal definitions of trajectory persistence and representational risk; presents a five-profile attacker taxonomy; and develops a unified threat model drawing on MITRE ATLAS and the OWASP LLM Top 10. We provide an empirical proof-of-concept demonstrating trajectory-persistent adversarial attacks on a GRU-based RSSM ($\\mathcal{A}_1 = 2.26\\times$ amplification, $-59.5\\%$ reward reduction under adversarial fine-tuning), validate architecture-dependence via a stochastic RSSM proxy ($\\mathcal{A}_1 = 0.65\\times$), and probe a real DreamerV3 checkpoint (non-zero action drift confirmed). We propose interdisciplinary mitigations spanning adversarial hardening, alignment engineering, NIST AI RMF and EU AI Act governance, and human-factors design, arguing that world models require the same rigour as flight-control software or medical devices.","descriptionType":"Abstract"},{"description":"version 2, 29 pages, 1 figure (6 panels), 3 tables. Empirical proof-of-concept on GRU/RSSM/DreamerV3 architectures","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.01346","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-03T01:46:00Z","registered":"2026-04-03T01:46:01Z","published":null,"updated":"2026-04-08T02:37:17Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2604.00993","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.00993","identifiers":[{"identifier":"2604.00993","identifierType":"arXiv"}],"creators":[{"name":"Nousiainen, Jalo","nameType":"Personal","givenName":"Jalo","familyName":"Nousiainen","affiliation":[],"nameIdentifiers":[]},{"name":"Taskin, Iremsu","nameType":"Personal","givenName":"Iremsu","familyName":"Taskin","affiliation":[],"nameIdentifiers":[]},{"name":"Kasper, Markus","nameType":"Personal","givenName":"Markus","familyName":"Kasper","affiliation":[],"nameIdentifiers":[]},{"name":"De Xivry, Gilles Orban","nameType":"Personal","givenName":"Gilles Orban","familyName":"De Xivry","affiliation":[],"nameIdentifiers":[]},{"name":"Absil, Olivier","nameType":"Personal","givenName":"Olivier","familyName":"Absil","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Focal plane wavefront control with model-based reinforcement learning"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Instrumentation and Methods for Astrophysics (astro-ph.IM)","subjectScheme":"arXiv"},{"lang":"en","subject":"Earth and Planetary Astrophysics (astro-ph.EP)","subjectScheme":"arXiv"},{"lang":"en","subject":"Machine Learning (cs.LG)","subjectScheme":"arXiv"},{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Physical sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-04-01T14:55:15Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-08T00:55:58Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging instruments on extremely large telescopes. Most such exoplanets orbit close to their host stars, where their observation is limited by fast-moving atmospheric speckles and quasi-static non-common-path aberrations (NCPA). Conventional NCPA correction methods often use mechanical mirror probes, which compromise performance during operation. This work presents machine-learning-based NCPA control methods that automatically detect and correct both dynamic and static NCPA errors by leveraging sequential phase diversity. We extend previous work in reinforcement learning for AO to focal plane control. A new model-based RL algorithm, Policy Optimization for NCPAs (PO4NCPA), interprets the focal-plane image as input data and, through sequential phase diversity, determines phase corrections that optimize both non-coronagraphic and post-coronagraphic PSFs without prior system knowledge. Further, we demonstrate the effectiveness of this approach by numerically simulating static NCPA errors on a ground-based telescope and an infrared imager affected by water-vapor-induced seeing (dynamic NCPAs). Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal-plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamics NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator in these metrics. The method remains effective for the ELT pupil, vector vortex coronagraph, and under photon and background noise. PO4NCPA is model-free and can be directly applied to standard imaging as well as to any coronagraph. Its sub-millisecond inference times and performance also make it suitable for real-time low-order correction of atmospheric turbulence beyond HCI.","descriptionType":"Abstract"},{"description":"13 pages, 11 figures accepted by A\u0026amp;A","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.00993","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-02T02:14:25Z","registered":"2026-04-02T02:14:26Z","published":null,"updated":"2026-04-08T02:37:11Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2604.00813","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.00813","identifiers":[{"identifier":"2604.00813","identifierType":"arXiv"}],"creators":[{"name":"Zuo, Sicheng","nameType":"Personal","givenName":"Sicheng","familyName":"Zuo","affiliation":[],"nameIdentifiers":[]},{"name":"Xie, Zixun","nameType":"Personal","givenName":"Zixun","familyName":"Xie","affiliation":[],"nameIdentifiers":[]},{"name":"Zheng, Wenzhao","nameType":"Personal","givenName":"Wenzhao","familyName":"Zheng","affiliation":[],"nameIdentifiers":[]},{"name":"Xu, Shaoqing","nameType":"Personal","givenName":"Shaoqing","familyName":"Xu","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Fang","nameType":"Personal","givenName":"Fang","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Hanbing","nameType":"Personal","givenName":"Hanbing","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Chen, Long","nameType":"Personal","givenName":"Long","familyName":"Chen","affiliation":[],"nameIdentifiers":[]},{"name":"Yang, Zhi-Xin","nameType":"Personal","givenName":"Zhi-Xin","familyName":"Yang","affiliation":[],"nameIdentifiers":[]},{"name":"Lu, Jiwen","nameType":"Personal","givenName":"Jiwen","familyName":"Lu","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Computer Vision and Pattern Recognition (cs.CV)","subjectScheme":"arXiv"},{"lang":"en","subject":"Artificial Intelligence (cs.AI)","subjectScheme":"arXiv"},{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-04-01T12:21:26Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-02T00:48:52Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T14:59:28Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T01:02:56Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"End-to-end autonomous driving has evolved from the conventional paradigm based on sparse perception into vision-language-action (VLA) models, which focus on learning language descriptions as an auxiliary task to facilitate planning. In this paper, we propose an alternative Vision-Geometry-Action (VGA) paradigm that advocates dense 3D geometry as the critical cue for autonomous driving. As vehicles operate in a 3D world, we think dense 3D geometry provides the most comprehensive information for decision-making. However, most existing geometry reconstruction methods (e.g., DVGT) rely on computationally expensive batch processing of multi-frame inputs and cannot be applied to online planning. To address this, we introduce a streaming Driving Visual Geometry Transformer (DVGT-2), which processes inputs in an online manner and jointly outputs dense geometry and trajectory planning for the current frame. We employ temporal causal attention and cache historical features to support on-the-fly inference. To further enhance efficiency, we propose a sliding-window streaming strategy and use historical caches within a certain interval to avoid repetitive computations. Despite the faster speed, DVGT-2 achieves superior geometry reconstruction performance on various datasets. The same trained DVGT-2 can be directly applied to planning across diverse camera configurations without fine-tuning, including closed-loop NAVSIM and open-loop nuScenes benchmarks.","descriptionType":"Abstract"},{"description":"Code is available at https://github.com/wzzheng/DVGT","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.00813","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-02T02:10:11Z","registered":"2026-04-02T02:10:11Z","published":null,"updated":"2026-04-08T02:37:09Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.26788","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.26788","identifiers":[{"identifier":"2603.26788","identifierType":"arXiv"}],"creators":[{"name":"Wu, Feng","nameType":"Personal","givenName":"Feng","familyName":"Wu","affiliation":[],"nameIdentifiers":[]},{"name":"Zuo, Wei","nameType":"Personal","givenName":"Wei","familyName":"Zuo","affiliation":[],"nameIdentifiers":[]},{"name":"Yang, Wenliang","nameType":"Personal","givenName":"Wenliang","familyName":"Yang","affiliation":[],"nameIdentifiers":[]},{"name":"Xiao, Jun","nameType":"Personal","givenName":"Jun","familyName":"Xiao","affiliation":[],"nameIdentifiers":[]},{"name":"Liu, Yang","nameType":"Personal","givenName":"Yang","familyName":"Liu","affiliation":[],"nameIdentifiers":[]},{"name":"Zeng, Xinhua","nameType":"Personal","givenName":"Xinhua","familyName":"Zeng","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"ReMemNav: A Rethinking and Memory-Augmented Framework for Zero-Shot Object Navigation"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"lang":"en","subject":"Computer Vision and Pattern Recognition (cs.CV)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-25T09:07:32Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-31T00:03:06Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T12:54:16Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:53:43Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Zero-shot object navigation requires agents to locate unseen target objects in unfamiliar environments without prior maps or task-specific training which remains a significant challenge. Although recent advancements in vision-language models(VLMs) provide promising commonsense reasoning capabilities for this task, these models still suffer from spatial hallucinations, local exploration deadlocks, and a disconnect between high-level semantic intent and low-level control. In this regard, we propose a novel hierarchical navigation framework named ReMemNav, which seamlessly integrates panoramic semantic priors and episodic memory with VLMs. We introduce the Recognize Anything Model to anchor the spatial reasoning process of the VLM. We also design an adaptive dual-modal rethinking mechanism based on an episodic semantic buffer queue. The proposed mechanism actively verifies target visibility and corrects decisions using historical memory to prevent deadlocks. For low-level action execution, ReMemNav extracts a sequence of feasible actions using depth masks, allowing the VLM to select the optimal action for mapping into actual spatial movement. Extensive evaluations on HM3D and MP3D demonstrate that ReMemNav outperforms existing training-free zero-shot baselines in both success rate and exploration efficiency. Specifically, we achieve significant absolute performance improvements, with SR and SPL increasing by 1.7% and 7.0% on HM3D v0.1, 18.2% and 11.1% on HM3D v0.2, and 8.7% and 7.9% on MP3D.","descriptionType":"Abstract"},{"description":"8 pages, 5 figures","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.26788","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-31T03:05:24Z","registered":"2026-03-31T03:05:25Z","published":null,"updated":"2026-04-08T02:36:38Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.26684","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.26684","identifiers":[{"identifier":"2603.26684","identifierType":"arXiv"}],"creators":[{"name":"Salanova, Fernando","nameType":"Personal","givenName":"Fernando","familyName":"Salanova","affiliation":[],"nameIdentifiers":[]},{"name":"Montijano, Eduardo","nameType":"Personal","givenName":"Eduardo","familyName":"Montijano","affiliation":[],"nameIdentifiers":[]},{"name":"Mahulea, Cristian","nameType":"Personal","givenName":"Cristian","familyName":"Mahulea","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Decoupling Geometric Planning and Execution in Scalable Multi-Agent Path Finding"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Multiagent Systems (cs.MA)","subjectScheme":"arXiv"},{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-11T11:04:54Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-31T00:00:32Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T07:20:56Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:34:08Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution Share Alike 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-sa-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Multi-Agent Path Finding (MAPF) requires collision-free trajectories for multiple agents on a shared graph, often with the objective of minimizing the sum-of-costs (SOC). Many optimal and bounded-suboptimal solvers rely on time-expanded models and centralized conflict resolution, which limits scalability in large or dense instances. We propose a hybrid prioritized framework that separates \\emph{geometric planning} from \\emph{execution-time conflict resolution}. In the first stage, \\emph{Geometric Conflict Preemption (GCP)} plans agents sequentially with A* on the original graph while inflating costs for transitions entering vertices used by higher-priority paths, encouraging spatial detours without explicit time reasoning. In the second stage, a \\emph{Decentralized Local Controller (DLC)} executes the geometric paths using per-vertex FIFO authorization queues and inserts wait actions to avoid vertex and edge-swap conflicts. Experiments on standard benchmark maps with up to 1000 agents show that the method scales with an near-linear runtime trend and attains a 100\\% success rate on instances satisfying the geometric feasibility assumption. Page of the project: https://sites.google.com/unizar.es/multi-agent-path-finding/home","descriptionType":"Abstract"},{"description":"6 pages, 3 figures, WODES conference paper","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.26684","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-31T03:02:58Z","registered":"2026-03-31T03:02:59Z","published":null,"updated":"2026-04-08T02:36:37Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.26320","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.26320","identifiers":[{"identifier":"2603.26320","identifierType":"arXiv"}],"creators":[{"name":"Chen, Jiayi","nameType":"Personal","givenName":"Jiayi","familyName":"Chen","affiliation":[],"nameIdentifiers":[]},{"name":"Song, Wenxuan","nameType":"Personal","givenName":"Wenxuan","familyName":"Song","affiliation":[],"nameIdentifiers":[]},{"name":"Chen, Shuai","nameType":"Personal","givenName":"Shuai","familyName":"Chen","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Jingbo","nameType":"Personal","givenName":"Jingbo","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Zhijun","nameType":"Personal","givenName":"Zhijun","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Haoang","nameType":"Personal","givenName":"Haoang","familyName":"Li","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"DFM-VLA: Iterative Action Refinement for Robot Manipulation via Discrete Flow Matching"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"lang":"en","subject":"Computer Vision and Pattern Recognition (cs.CV)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-27T11:38:43Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-30T00:44:46Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-03-31T14:58:55Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-01T01:02:26Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04-07T08:10:41Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2026-04-08T00:37:16Z","dateType":"Updated","dateInformation":"v3"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"3","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Vision--Language--Action (VLA) models that encode actions using a discrete tokenization scheme are increasingly adopted for robotic manipulation, but existing decoding paradigms remain fundamentally limited. Whether actions are decoded sequentially by autoregressive VLAs or in parallel by discrete diffusion VLAs, once a token is generated, it is typically fixed and cannot be revised in subsequent iterations, so early token errors cannot be effectively corrected later. We propose DFM-VLA, a discrete flow matching VLA for iterative refinement of action tokens. DFM-VLA~models a token-level probability velocity field that dynamically updates the full action sequence across refinement iterations. We investigate two ways to construct the velocity field: an auxiliary velocity-head formulation and an action-embedding-guided formulation. Our framework further adopts a two-stage decoding strategy with an iterative refinement stage followed by deterministic validation for stable convergence. Extensive experiments on CALVIN, LIBERO, and real-world manipulation tasks show that DFM-VLA consistently outperforms strong autoregressive, discrete diffusion, and continuous diffusion baselines in manipulation performance while retaining high inference efficiency. In particular, DFM-VLA achieves an average success length of 4.44 on CALVIN and an average success rate of 95.7\\% on LIBERO, highlighting the value of action refinement via discrete flow matching for robotic manipulation. Our project is available https://chris1220313648.github.io/DFM-VLA/","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.26320","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-30T01:58:12Z","registered":"2026-03-30T01:58:13Z","published":null,"updated":"2026-04-08T02:36:34Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.25661","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.25661","identifiers":[{"identifier":"2603.25661","identifierType":"arXiv"}],"creators":[{"name":"Song, Wenxuan","nameType":"Personal","givenName":"Wenxuan","familyName":"Song","affiliation":[],"nameIdentifiers":[]},{"name":"Chen, Jiayi","nameType":"Personal","givenName":"Jiayi","familyName":"Chen","affiliation":[],"nameIdentifiers":[]},{"name":"Chen, Shuai","nameType":"Personal","givenName":"Shuai","familyName":"Chen","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Jingbo","nameType":"Personal","givenName":"Jingbo","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Ding, Pengxiang","nameType":"Personal","givenName":"Pengxiang","familyName":"Ding","affiliation":[],"nameIdentifiers":[]},{"name":"Zhao, Han","nameType":"Personal","givenName":"Han","familyName":"Zhao","affiliation":[],"nameIdentifiers":[]},{"name":"Qin, Yikai","nameType":"Personal","givenName":"Yikai","familyName":"Qin","affiliation":[],"nameIdentifiers":[]},{"name":"Zheng, Xinhu","nameType":"Personal","givenName":"Xinhu","familyName":"Zheng","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Donglin","nameType":"Personal","givenName":"Donglin","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Yan","nameType":"Personal","givenName":"Yan","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Haoang","nameType":"Personal","givenName":"Haoang","familyName":"Li","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"lang":"en","subject":"Computer Vision and Pattern Recognition (cs.CV)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-26T17:14:57Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-27T01:08:13Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-03-27T11:46:16Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-03-30T00:45:30Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04-07T08:13:17Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2026-04-08T00:37:35Z","dateType":"Updated","dateInformation":"v3"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"3","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning (SFT). Some advanced finetuning methods with auxiliary training objectives can improve performance and reduce the number of convergence steps. However, they typically incur significant computational overhead due to the additional losses from auxiliary tasks. To simultaneously achieve the enhanced capabilities of auxiliary training with the simplicity of standard SFT, we decouple the two objectives of auxiliary task training within the parameter space, namely, enhancing general capabilities and fitting task-specific action distributions. To deliver this goal, we only need to train the model to converge on a small-scale task set using two distinct training strategies. The difference between the resulting model parameters can then be interpreted as capability vectors provided by auxiliary tasks. These vectors are then merged with pretrained parameters to form a capability-enhanced meta model. Moreover, when standard SFT is augmented with a lightweight orthogonal regularization loss, the merged model attains performance comparable to auxiliary finetuned baselines with reduced computational overhead. Experimental results demonstrate that this approach is highly effective across diverse robot tasks. Project page: https://chris1220313648.github.io/Fast-dVLA/","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.25661","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-27T02:14:28Z","registered":"2026-03-27T02:14:29Z","published":null,"updated":"2026-04-08T02:36:30Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.24935","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.24935","identifiers":[{"identifier":"2603.24935","identifierType":"arXiv"}],"creators":[{"name":"Wu, Xiyang","nameType":"Personal","givenName":"Xiyang","familyName":"Wu","affiliation":[],"nameIdentifiers":[]},{"name":"Shi, Guangyao","nameType":"Personal","givenName":"Guangyao","familyName":"Shi","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Qingzi","nameType":"Personal","givenName":"Qingzi","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Zongxia","nameType":"Personal","givenName":"Zongxia","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Bedi, Amrit Singh","nameType":"Personal","givenName":"Amrit Singh","familyName":"Bedi","affiliation":[],"nameIdentifiers":[]},{"name":"Manocha, Dinesh","nameType":"Personal","givenName":"Dinesh","familyName":"Manocha","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-26T01:56:01Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-27T00:21:01Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T10:20:22Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:45:09Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Vision-language-action (VLA) models enable robots to follow natural-language instructions grounded in visual observations, but the instruction channel also introduces a critical vulnerability: small textual perturbations can alter downstream robot behavior. Systematic robustness evaluation therefore requires a black-box attacker that can generate minimal yet effective instruction edits across diverse VLA models. To this end, we present SABER, an agent-centric approach for automatically generating instruction-based adversarial attacks on VLA models under bounded edit budgets. SABER uses a GRPO-trained ReAct attacker to generate small, plausible adversarial instruction edits using character-, token-, and prompt-level tools under a bounded edit budget that induces targeted behavioral degradation, including task failure, unnecessarily long execution, and increased constraint violations. On the LIBERO benchmark across six state-of-the-art VLA models, SABER reduces task success by 20.6%, increases action-sequence length by 55%, and raises constraint violations by 33%, while requiring 21.1% fewer tool calls and 54.7% fewer character edits than strong GPT-based baselines. These results show that small, plausible instruction edits are sufficient to substantially degrade robot execution, and that an agentic black-box pipeline offers a practical, scalable, and adaptive approach for red-teaming robotic foundation models. The codebase is publicly available at https://github.com/wuxiyang1996/SABER.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.24935","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-27T01:57:14Z","registered":"2026-03-27T01:57:14Z","published":null,"updated":"2026-04-08T02:36:28Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.24155","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.24155","identifiers":[{"identifier":"2603.24155","identifierType":"arXiv"}],"creators":[{"name":"Yadav, Harsh","nameType":"Personal","givenName":"Harsh","familyName":"Yadav","affiliation":[],"nameIdentifiers":[]},{"name":"Meisen, Tobias","nameType":"Personal","givenName":"Tobias","familyName":"Meisen","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-25T10:24:16Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-26T00:44:27Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T08:33:33Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:38:48Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-loop settings. Furthermore, relying on static datasets or non-reactive log-replay simulators severs the interactive loop, preventing the ego agent from learning to actively negotiate surrounding traffic. In this work, we propose an on-policy closed-loop training paradigm optimized for high-frequency, receding horizon ego prediction. To ground the ego prediction in a realistic representation of traffic interactions and to achieve reactive consistency, we introduce a goal-oriented, transformer-based scene decoder, resulting in an inherently reactive training simulation. By exposing the ego agent to a mixture of open-loop data and simulated, self-induced states, the model learns recovery behaviors to correct its own execution errors. Extensive evaluation demonstrates that closed-loop training significantly enhances collision avoidance capabilities at high replanning frequencies, yielding relative collision rate reductions of up to 27.0% on nuScenes and 79.5% in dense DeepScenario intersections compared to open-loop baselines. Additionally, we show that a hybrid simulation combining reactive with non-reactive surrounding agents achieves optimal balance between immediate interactivity and long-term behavioral stability.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.24155","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-26T02:10:56Z","registered":"2026-03-26T02:10:57Z","published":null,"updated":"2026-04-08T02:36:22Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2603.03181","type":"dois","attributes":{"doi":"10.48550/arxiv.2603.03181","identifiers":[{"identifier":"2603.03181","identifierType":"arXiv"}],"creators":[{"name":"Liu, Yichang","nameType":"Personal","givenName":"Yichang","familyName":"Liu","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Tianyu","nameType":"Personal","givenName":"Tianyu","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Ye, Ziyi","nameType":"Personal","givenName":"Ziyi","familyName":"Ye","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Yawei","nameType":"Personal","givenName":"Yawei","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Jiang, Yu-Gang","nameType":"Personal","givenName":"Yu-Gang","familyName":"Jiang","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Shouyan","nameType":"Personal","givenName":"Shouyan","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Fu, Yanwei","nameType":"Personal","givenName":"Yanwei","familyName":"Fu","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"lang":"en","subject":"Signal Processing (eess.SP)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-03T17:41:42Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-03-04T02:04:05Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T07:50:13Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:36:02Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-03","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement. Motivated by the promise of BCI-driven robotics to enhance human-robot interaction, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner within an online streaming pipeline. This establishes a dual-channel intent interface that translates visual intent into robotic actions, with VI identifying objects for grasping and MI determining placement poses, enabling intuitive control over both what to grasp and where to place. The system operates solely on EEG via a cue-free imagery protocol, achieving integration and online validation. Implemented on a Base robotic platform and evaluated across diverse scenarios, including occluded targets or varying participant postures, the system achieves online decoding accuracies of 40.23% (VI) and 62.59% (MI), with an end-to-end task success rate of 20.88%. These results demonstrate that high-level visual cognition can be decoded in real time and translated into executable robot commands, bridging the gap between neural signals and physical interaction, and validating the flexibility of a purely imagery-based BCI paradigm for practical human-robot collaboration.","descriptionType":"Abstract"},{"description":"ICRA 2026","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2603.03181","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-04T03:08:49Z","registered":"2026-03-04T03:08:55Z","published":null,"updated":"2026-04-08T02:35:25Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2601.16242","type":"dois","attributes":{"doi":"10.48550/arxiv.2601.16242","identifiers":[{"identifier":"2601.16242","identifierType":"arXiv"}],"creators":[{"name":"Yaqubi, S.","nameType":"Personal","givenName":"S.","familyName":"Yaqubi","affiliation":[],"nameIdentifiers":[]},{"name":"Mattila, J.","nameType":"Personal","givenName":"J.","familyName":"Mattila","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Scalable Screw-Theoretic Synthesis for PDE-Based Dynamic Modeling of Multibody Flexible Manipulators"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-01-22T09:05:25Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-01-26T01:00:44Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-03-24T12:37:27Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-03-25T00:56:54Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04-07T05:50:16Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2026-04-08T00:30:29Z","dateType":"Updated","dateInformation":"v3"},{"date":"2026-01","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This paper presents a novel and scalable screw-theoretic multibody synthesis framework for PDE-based dynamic modeling of serial robotic manipulators with an arbitrary number of flexible links in three-dimensional space. The proposed approach systematically constructs screw-theoretic PDE models for individual flexible links and rigorously enforces holonomic joint constraints through interaction forces. The dynamics of each link are formulated using a set of dual screws expressed in body-fixed coordinates: one describing the motion of the body-fixed frame relative to the inertial frame, a second relating the body-fixed frame to the undeformed configuration, and a third capturing elastic deformations. By expressing the system energy and applying variational principles, the governing dynamics of each link had been previously derived in a unified manner. Synthesizing the individual link models yields an infinitely scalable multibody representation capable of capturing both local (subsystem-level) and global (system-level) dynamics. The framework explicitly recovers all dynamic states, including the motion of each body-fixed frame and the distributed deformation fields of the flexible links. For computational tractability and mathematical rigor, the resulting governing equations are formulated as a semi-explicit index-1 differential-algebraic system. Furthermore, by applying separation of variables, the PDE model is recast as an abstract Cauchy problem, and well-posedness of the resulting system is established.","descriptionType":"Abstract"},{"description":"Submitted to Springer for peer review. Copyright might be transferred without notice","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2601.16242","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-01-26T02:25:26Z","registered":"2026-01-26T02:25:26Z","published":null,"updated":"2026-04-08T02:34:23Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2601.06748","type":"dois","attributes":{"doi":"10.48550/arxiv.2601.06748","identifiers":[{"identifier":"2601.06748","identifierType":"arXiv"}],"creators":[{"name":"Liu, Changyu","nameType":"Personal","givenName":"Changyu","familyName":"Liu","affiliation":[],"nameIdentifiers":[]},{"name":"Liu, Yiyang","nameType":"Personal","givenName":"Yiyang","familyName":"Liu","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Taowen","nameType":"Personal","givenName":"Taowen","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Zhuang, Qiao","nameType":"Personal","givenName":"Qiao","familyName":"Zhuang","affiliation":[],"nameIdentifiers":[]},{"name":"Liang, James Chenhao","nameType":"Personal","givenName":"James Chenhao","familyName":"Liang","affiliation":[],"nameIdentifiers":[]},{"name":"Yang, Wenhao","nameType":"Personal","givenName":"Wenhao","familyName":"Yang","affiliation":[],"nameIdentifiers":[]},{"name":"Xu, Renjing","nameType":"Personal","givenName":"Renjing","familyName":"Xu","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Qifan","nameType":"Personal","givenName":"Qifan","familyName":"Wang","affiliation":[],"nameIdentifiers":[]},{"name":"Liu, Dongfang","nameType":"Personal","givenName":"Dongfang","familyName":"Liu","affiliation":[],"nameIdentifiers":[]},{"name":"Han, Cheng","nameType":"Personal","givenName":"Cheng","familyName":"Han","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-01-11T01:51:30Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-01-13T01:38:12Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-01-13T03:57:18Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-01-14T01:17:52Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04-07T03:39:20Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2026-04-08T00:24:58Z","dateType":"Updated","dateInformation":"v3"},{"date":"2026-01","dateType":"Available","dateInformation":"v1"},{"date":"2026","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"3","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Vision-Language-Action models have recently emerged as a powerful paradigm for general-purpose robot learning, enabling agents to map visual observations and natural-language instructions into executable robotic actions. Though popular, they are primarily trained via supervised fine-tuning or training-time reinforcement learning, requiring explicit fine-tuning phases, human interventions, or controlled data collection. Consequently, existing methods remain unsuitable for challenging simulated- or physical-world deployments, where robots must respond autonomously and flexibly to evolving environments. To address this limitation, we introduce a Test-Time Reinforcement Learning for VLAs (TT-VLA), a framework that enables on-the-fly policy adaptation during inference. TT-VLA formulates a dense reward mechanism that leverages step-by-step task-progress signals to refine action policies during test time while preserving the SFT/RL-trained priors, making it an effective supplement to current VLA models. Empirical results show that our approach enhances overall adaptability, stability, and task success in dynamic, previously unseen scenarios under simulated and real-world settings. We believe TT-VLA offers a principled step toward self-improving, deployment-ready VLAs.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2601.06748","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-01-13T03:46:39Z","registered":"2026-01-13T03:46:39Z","published":null,"updated":"2026-04-08T02:34:04Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2512.18836","type":"dois","attributes":{"doi":"10.48550/arxiv.2512.18836","identifiers":[{"identifier":"2512.18836","identifierType":"arXiv"}],"creators":[{"name":"Teng, Jingjia","nameType":"Personal","givenName":"Jingjia","familyName":"Teng","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Yang","nameType":"Personal","givenName":"Yang","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Bian, Yougang","nameType":"Personal","givenName":"Yougang","familyName":"Bian","affiliation":[],"nameIdentifiers":[]},{"name":"Hu, Manjiang","nameType":"Personal","givenName":"Manjiang","familyName":"Hu","affiliation":[],"nameIdentifiers":[]},{"name":"Hu, Yingbai","nameType":"Personal","givenName":"Yingbai","familyName":"Hu","affiliation":[],"nameIdentifiers":[]},{"name":"Li, Guofa","nameType":"Personal","givenName":"Guofa","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Jianqiang","nameType":"Personal","givenName":"Jianqiang","familyName":"Wang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Multimodal Classification Network Guided Trajectory Planning for Four-Wheel Independent Steering Autonomous Parking Considering Obstacle Attributes"}],"publisher":"arXiv","container":{},"publicationYear":2025,"subjects":[{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-12-21T17:45:57Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2025-12-23T01:53:48Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-01-04T13:31:49Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-01-06T01:42:29Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04-06T19:26:51Z","dateType":"Withdrawn","dateInformation":"v3; The manuscript in this current form requires substantial revision. For this reason, I request the withdrawal of the submission to allow for comprehensive improvement before resubmission"},{"date":"2026-04-08T00:06:05Z","dateType":"Updated","dateInformation":"v3"},{"date":"2025-12","dateType":"Available","dateInformation":"v1"},{"date":"2025","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"3","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Four-wheel Independent Steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over the low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, leading to suboptimal efficiency or planning failures. To address this issue, we propose a novel multimodal trajectory planning framework that employs a neural network for scene perception, combines 4WIS hybrid A* search to generate a warm start, and utilizes an optimal control problem (OCP) for trajectory optimization. Specifically, a multimodal perception network fusing visual information and vehicle states is employed to capture semantic and contextual scene understanding, enabling the planner to adapt the strategy according to scene complexity (hard or easy task). For hard tasks, guided points are introduced to decompose complex tasks into local subtasks, improving the search efficiency. The multiple steering modes of 4WIS vehicles, Ackermann, diagonal, and zero-turn, are also incorporated as kinematically feasible motion primitives. Moreover, a hierarchical obstacle handling strategy, which categorizes obstacles as \"non-traversable\", \"crossable\", and \"drive-over\", is incorporated into the node expansion process, explicitly linking obstacle attributes to planning actions to enable efficient decisions. Furthermore, to address dynamic obstacles with motion uncertainty, we introduce a probabilistic risk field model, constructing risk-aware driving corridors that serve as linear collision constraints in OCP. Experimental results demonstrate the proposed framework's effectiveness in generating safe, efficient, and smooth trajectories for 4WIS vehicles, especially in constrained environments.","descriptionType":"Abstract"},{"description":"The manuscript in this current form requires substantial revision. For this reason, I request the withdrawal of the submission to allow for comprehensive improvement before resubmission","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2512.18836","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-12-23T03:50:21Z","registered":"2025-12-23T03:50:22Z","published":null,"updated":"2026-04-08T02:33:20Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2512.16811","type":"dois","attributes":{"doi":"10.48550/arxiv.2512.16811","identifiers":[{"identifier":"2512.16811","identifierType":"arXiv"}],"creators":[{"name":"Qian, Jingjing","nameType":"Personal","givenName":"Jingjing","familyName":"Qian","affiliation":[],"nameIdentifiers":[]},{"name":"Han, Boyao","nameType":"Personal","givenName":"Boyao","familyName":"Han","affiliation":[],"nameIdentifiers":[]},{"name":"Shi, Chen","nameType":"Personal","givenName":"Chen","familyName":"Shi","affiliation":[],"nameIdentifiers":[]},{"name":"Xiao, Lei","nameType":"Personal","givenName":"Lei","familyName":"Xiao","affiliation":[],"nameIdentifiers":[]},{"name":"Yang, Long","nameType":"Personal","givenName":"Long","familyName":"Yang","affiliation":[],"nameIdentifiers":[]},{"name":"Shi, Shaoshuai","nameType":"Personal","givenName":"Shaoshuai","familyName":"Shi","affiliation":[],"nameIdentifiers":[]},{"name":"Jiang, Li","nameType":"Personal","givenName":"Li","familyName":"Jiang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"GeoPredict: Leveraging Predictive Kinematics and 3D Gaussian Geometry for Precise VLA Manipulation"}],"publisher":"arXiv","container":{},"publicationYear":2025,"subjects":[{"lang":"en","subject":"Computer Vision and Pattern Recognition (cs.CV)","subjectScheme":"arXiv"},{"lang":"en","subject":"Robotics (cs.RO)","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-12-18T17:51:42Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2025-12-19T02:00:06Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-04-07T13:11:17Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-04-08T00:54:58Z","dateType":"Updated","dateInformation":"v2"},{"date":"2025-12","dateType":"Available","dateInformation":"v1"},{"date":"2025","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Article","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rights":"arXiv.org perpetual, non-exclusive license","rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"descriptions":[{"description":"Vision-Language-Action (VLA) models achieve strong generalization in robotic manipulation but remain largely reactive and 2D-centric, making them unreliable in tasks that require precise 3D reasoning. We propose GeoPredict, a geometry-aware VLA framework that augments a continuous-action policy with predictive kinematic and geometric priors. GeoPredict introduces a trajectory-level module that encodes motion history and predicts multi-step 3D keypoint trajectories of robot arms, and a predictive 3D Gaussian geometry module that forecasts workspace geometry with track-guided refinement along future keypoint trajectories. These predictive modules serve exclusively as training-time supervision through depth-based rendering, while inference requires only lightweight additional query tokens without invoking any 3D decoding. Experiments on RoboCasa Human-50, LIBERO, and real-world manipulation tasks show that GeoPredict consistently outperforms strong VLA baselines, especially in geometry-intensive and spatially demanding scenarios.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2512.16811","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-12-19T03:07:20Z","registered":"2025-12-19T03:07:20Z","published":null,"updated":"2026-04-08T02:33:11Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}}],"meta":{"total":90493,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois?query=subjects.subject%3Arobot%2A","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026query=subjects.subject%3Arobot%2A"}}