Publication:
Physical intelligence as a new paradigm

dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.kuauthorSitti, Metin
dc.contributor.kuprofileFaculty Member
dc.contributor.schoolcollegeinstituteSchool of Medicine
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.yokid297104
dc.date.accessioned2024-11-09T11:55:57Z
dc.date.issued2021
dc.description.abstractIntelligence of physical agents, such as human-made (e.g., robots, autonomous cars) and biological (e.g., animals, plants) ones, is not only enabled by their computational intelligence (CI) in their brain, but also by their physical intelligence (PI) encoded in their body. Therefore, it is essential to advance the PI of human-made agents as much as possible, in addition to their CI, to operate them in unstructured and complex real-world environments like the biological agents. This article gives a perspective on what PI paradigm is, when PI can be more significant and dominant in physical and biological agents at different length scales and how bioinspired and abstract PI methods can be created in agent bodies. PI paradigm aims to synergize and merge many research fields, such as mechanics, materials science, robotics, mechanical design, fluidics, active matter, biology, self-assembly and collective systems, to enable advanced PI capabilities in human-made agent bodies, comparable to the ones observed in biological organisms. Such capabilities would progress the future robots and other machines beyond what can be realized using the current frameworks.
dc.description.fulltextYES
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuEU
dc.description.sponsorshipEuropean Union (EU)
dc.description.sponsorshipHorizon 2020
dc.description.sponsorshipEuropean Research Council (ERC)
dc.description.sponsorshipAdvanced Grant
dc.description.sponsorshipSoMMoR Project
dc.description.sponsorshipGerman Research Foundation (DFG)
dc.description.sponsorshipSoft Material Robotic Systems (SPP 2100) Program
dc.description.sponsorshipMax Planck Society
dc.description.versionPublisher version
dc.description.volume46
dc.formatpdf
dc.identifier.doi10.1016/j.eml.2021.101340
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR03006
dc.identifier.issn2352-4316
dc.identifier.linkhttps://doi.org/10.1016/j.eml.2021.101340
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85105689950
dc.identifier.urihttps://hdl.handle.net/20.500.14288/838
dc.identifier.wos661866500014
dc.keywordsPhysical intelligence
dc.keywordsMechanics
dc.keywordsMeta materials
dc.keywordsMultistability
dc.keywordsMechanical memory
dc.keywordsMechanical computation
dc.languageEnglish
dc.publisherElsevier
dc.relation.grantno834531
dc.relation.grantno2197/3-1
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/9652
dc.sourceExtreme Mechanics Letters
dc.subjectEngineering
dc.subjectMaterials science
dc.subjectMechanics
dc.titlePhysical intelligence as a new paradigm
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.authorid0000-0001-8249-3854
local.contributor.kuauthorSitti, Metin
relation.isOrgUnitOfPublicationba2836f3-206d-4724-918c-f598f0086a36
relation.isOrgUnitOfPublication.latestForDiscoveryba2836f3-206d-4724-918c-f598f0086a36

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