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.
Pfupiso
Izvo zvine basa nekuti zvinounza iyo yakafara pfungwa yenheyo modhi kumakina emuviri, ichirega imwe modhi idzore robhoti pamabasa mazhinji pane kunyora nemaoko maitiro ega ega.
Kudzika Kwakadzika
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.
Strategic Impact
Kumhanya uye chiyero
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero.
Vaka sarudzo
Zvikwata zvekugadzira zvinogona prototype pfungwa nekukurumidza nekudzokororwa kwemaoko mashoma.
Team uye workflow
Mashandisirwo anogona kushandisa masaini emifananidzo nemavhidhiyo ayo aimbove akaoma kugadzirisa.
Ramangwana reVision-Language-Action Models yeRobhoti
Tarisira makuru makuru-embodiment datasets (iyo Vhura X-Embodiment kuedza yatove nedhata kubva ku22+ mhando dzemarobhoti) saka imwe modhi inotyaira maoko, humanoids, uye nharembozha. Tsvagiridzo inosundira kune inokurumidza kufungidzira kune chaiyo-nguva kutonga, yakapfuma 3D uye tactile mapikisi, uye ekufunga cheni uko modhi 'inofunga' isati yaita. Chinangwa igwaro rimwe chete rezvematongerwo enyika raunogona kusimudzira muChirungu chakajeka, ne-on-the-fly gadziriso, sekunge kutaura nemubatsiri.
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.
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.
Edzai nedata rinoenderana nemamiriro chaiwo ekugadzira.
Wedzera ongororo yemunhu kune yakaderera-kusavimbika kana yakakwirira-inokanganisa kufanotaura.
Tevera modhi kudonha uye simbisa mushure mekuchinja kwekamera kana dataset.
Ramba Uchiongorora
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Gaidhi rinotevera
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Mibvunzo inowanzo bvunzwa
Chii chinonzi 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.
Ndeapi marudzi matatu ekupinza/zvinobuda anotsanangura Vision-Language-Action (VLA) modhi?
A VLA inotora chiratidzo (mifananidzo) pamwe nechinangwa chemutauro uye zvinobuda zviito (motor command), kubatanidza maonero, kuraira-kutevera, uye kutonga.
Ko Google DeepMind's RT-2 yakamiririra sei zviito zvemarobhoti kuti transformer igozvigadzira?
RT-2 yakasungira chidimbu chega chega kuita zviratidzo (semuenzaniso, 256 mabhini) uye ndokuaburitsa setambo, ichisiya mutauro-modhiyo muchina kufanotaura zviito semazwi.
Chii chinonzi 'emergent transfer' chinorevei mumamiriro ezvinhu eVLAs?
Nekuti maVLA anotangira pamhando dzemutauro wechiratidzo akadzidziswa pawebhu, ruzivo rwakaita sekuziva marogo kana kunzwisisa 'idiki' inotamiswa kuenda kumabasa ekunyengera asina kumboonekwa mumarobhoti data.
Chii chakakosha mukana wepi-0's diffusion/flow-matching action musoro uchienzaniswa neasing discrete action tokens?
Panzvimbo peimwe nhanho yakasarudzika panguva, pi-0 inogadzira inoenderera chiito chunks pakakwirira frequency, ichigonesa kufamba kwakapfava uye kwakanyanyisa.
Sei iyo Yakavhurika X-Embodiment dataset ine basa kune VLAs?
Vhura X-Embodiment inosanganisa trajectories kubva kumarobhoti akawanda akasiyana, ichibatsira marobhoti echitima ayo anoendesa nepakati maoko, nharembozha, uye mamwe embodiments.