Overview
Vision-Language-Action (VLA) modhi mahombe neural network anotora mifananidzo yekamera pamwe nerairo rakanyorwa uye zvakananga kubuda robhoti mota mirairo. Izvo zvine basa nekuti zvinounza iyo yakafara nzwisiso yemamodhi enheyo kumakina emuviri, ichirega imwe modhi idzore robhoti pamabasa mazhinji pane kunyora nemaoko maitiro ega ega.
Vision-Language-Action Models yeRobhoti ndeyekombuta-kuona mafambiro anodudzira kana kuburitsa midhiya yekuona yekuongorora, mashandiro, uye kugadzira.
Deep Dive
VLA modhi inosanganisa hova nhatu: kuona (mafuremu ekamera), mutauro (chinangwa chakaita se'isa kapu musingi'), uye chiito (majoint angles, gripper open/close, kana end-effector velocities). Google DeepMind's RT-2 yaive yakakosha: yakatora modhi yemutauro wechiratidzo yakadzidziswa pawebhusaiti yemifananidzo uye zvinyorwa, ndokuigadzirisa pamwe chete pamarobhoti trajectories kuitira kuti network imwe cheteyo inogona kupindura 'chibereko chipi ichi?' zvakare inoburitsa zviito zvakaratidzwa sechinyorwa. Vhura modhi seOpenVLA (7B paramita) uye Physical Intelligence's pi-0 inoteverwa. Zvikuru, aya mamodheru anoratidza 'emergent' kutamiswa: ruzivo rwewebhu (kuziva chiratidzo chemhando, kunzwisisa 'idiki') inotakura mukunyengedza, saka robhoti rinowedzera kune zvinhu uye mirairo yayasina kumboona panguva yekudzidzira marobhoti.
Technical Insight
MaVLA mazhinji anocherekedza zviito zvinoramba zvichiitika kuita tokens kuitira kuti shanduri igone kufanotaura ivo autoregressively, semashoko. RT-2 inomepu chimwe nechimwe chiitiko kune rimwe remabhini 256 uye inoaburitsa setambo yemavara. Madhizaini matsva senge pi-0 anosungira kupararira kana kuyerera-kufananidza 'nyanzvi yechiito' kumusoro kune yakaomeswa nemutauro wemutauro wemusana, ichigadzira yakatsetseka yepamusoro-frequency chiito chunks (semuenzaniso, 50 Hz) pachinzvimbo chenhanho imwe chete yakasarudzika, kuvandudza dexterity.
Mastering Vision-Language-Action Models yeRobhoti
Vision-Language-Action (VLA) modhi mahombe neural network anotora mifananidzo yekamera pamwe nerairo rakanyorwa uye zvakananga kubuda robhoti mota mirairo. Izvo zvine basa nekuti zvinounza iyo yakafara nzwisiso yemamodhi enheyo kumakina emuviri, ichirega imwe modhi idzore robhoti pamabasa mazhinji pane kunyora nemaoko maitiro ega ega. Vision-Language-Action Models yeRobhoti ndeyekombuta-kuona mafambiro anodudzira kana kuburitsa midhiya yekuona yekuongorora, mashandiro, uye kugadzira. Kuti uvake kunzwisisa kwakadzama, tora Vision-Language-Action Models yeRobhoti semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodikanwa, kujekesa fungidziro, uye patsanura zvinogona kuitwa nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Vision-Language-Action Models yeMarobhoti akaenzana nehuchokwadi hwekushanda semhando yedata, kusiyana kwemwenje, uye kuenderana kwemazita. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero. Panguva imwecheteyo, kodzero dzeMufananidzo uye kubvumirwa kunogona kuve njodzi dzepamutemo kana hunhu husina kujeka. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero.
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Zvikwata zvekugadzira zvinogona prototype pfungwa nekukurumidza nekudzokororwa kwemaoko mashoma.
Zvikwata zvekugadzira zvinogona prototype pfungwa nekukurumidza nekudzokororwa kwemaoko mashoma. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Mashandisirwo anogona kushandisa masaini emifananidzo nemavhidhiyo ayo aimbove akaoma kugadzirisa.
Mashandisirwo anogona kushandisa masaini emifananidzo nemavhidhiyo ayo aimbove akaoma kugadzirisa. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
RT-2 inodzora Google robhoti rekubikira 'kuendesa banana kunhamba 3' uchishandisa manhamba arakadzidzwa kubva pawebhu, kwete marobhoti demo.
OpenVLA, yakavhurika-sosi 7B modhi, yakanatswa-yakarongedzwa nemalebhu kuti imhanye tabletop pick-ne-nzvimbo pamaoko anodhura.
Physical Intelligence's pi-0 kupeta mbatya uye kuchenesa tafura nekusunga akawanda madiki hunyanzvi kubva kune imwechete rairo.
Ruoko rwemba yekuchengetera zvinhu rwakaudzwa kuti 'sarudza chinhu chisina kusimba' uye kuratidza kuti ndechipi chinhu chinobva pakuonekwa kwayo.
Maitiro Ekuita
Vision-Language-Action Models yeRobhoti mukuita
RT-2 inodzora Google robhoti rekubikira 'kuendesa banana kunhamba 3' vachishandisa manhamba arakadzidzwa kubva pawebhu, kwete marobhoti demo.
RT-2 inodzora Google robhoti rekubikira 'kufambisa banana kuenda kunhamba 3' uchishandisa manhamba arakadzidzwa kubva paweb text, kwete marobhoti demo Matimu anowanzo kuwana mibairo iri nani kana atsanangura mabhindauko emhando kumberi, chengetedza nzira yekukwira kwevanhu yekesi dzemupendero, uye kuronda zvese zvakawanikwa pakubereka uye mutengo wekukanganisa nekufamba kwenguva.
Vision-Language-Action Models yeRobhoti mukuita
OpenVLA, yakavhurika-sosi 7B modhi, yakanatswa-yakarongedzwa nemalebhu kuti imhanye tabletop pick-ne-nzvimbo pamaoko akaderera.
OpenVLA, yakavhurika-sosi 7B modhi, yakanatswa-yakarongedzwa nemalabhu ekumhanyisa tabletop pick-uye-nzvimbo pamaoko anodhura Matimu anowanzo kuwana mibairo iri nani kana achinge atsanangura zvikumbaridzo zvemhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye kukanganisa mutengo nekufamba kwenguva.
Vision-Language-Action Models yeRobhoti mukuita
Physical Intelligence's pi-0 kupeta mbatya uye kuchenesa tafura nekusunga akawanda madiki hunyanzvi kubva kune imwechete kuraira.
Physical Intelligence's pi-0 kupeta mbatya uye kujekesa tafura nekusunga akawanda hunyanzvi kubva kune imwechete yekuraira Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Vision-Language-Action Models yeRobhoti mukuita
Ruoko rwemumba inochengeterwa zvinhu rwakaudzwa kuti 'sarudza chinhu chisina kusimba zvakanyanya' uye zvichiratidza kuti ndechipi chinhu chinobva pakuonekwa kwacho.
Ruoko rwekuchengetera zvinhu rwakaudzwa kuti 'sarudza chinhu chisina kusimba' uye zvichireva kuti ndechipi chinhu chinobva pakuonekwa kwacho Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
Kodzero dzemifananidzo uye kubvumirwa kunogona kuve njodzi dzepamutemo kana provenance isina kujeka.
Kuita kwemuenzaniso kunogona kusiyanisa kupenya, huwandu hwevanhu, uye nharaunda.
Manyepo enhema anogona kusacherechedzwa kunze kwekunge zvikumbaridzo zvekuvimba zvikatariswa.
Implementation Roadmap
Tsanangura maitiro ekugamuchirwa echokwadi, kurangarira, uye mutengo wekukanganisa.
Tsanangura maitiro ekugamuchirwa echokwadi, kurangarira, uye mutengo wekukanganisa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Edzai nedata rinoenderana nemamiriro chaiwo ekugadzira.
Edzai nedata rinoenderana nemamiriro chaiwo ekugadzira. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Wedzera ongororo yemunhu kune yakaderera-kusavimbika kana yakakwirira-inokanganisa kufanotaura.
Wedzera ongororo yemunhu kune yakaderera-kusavimbika kana yakakwirira-inokanganisa kufanotaura. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Tevera modhi kudonha uye simbisa mushure mekuchinja kwekamera kana dataset.
Tevera modhi kudonha uye simbisa mushure mekuchinja kwekamera kana dataset. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.