Umbono-Ulimi-Action Models for Robotics
Amamodeli we-Vision-Language-Action (VLA) amanethiwekhi amakhulu e-neural athatha izithombe zekhamera kanye nomyalelo obhaliwe kanye nemiyalo yemoto yerobhothi ephuma ngokuqondile.
Uhlolojikelele
They matter because they bring the broad common sense of foundation models to physical machines, letting one model control a robot across many tasks instead of hand-coding each behavior.
I-Deep Dive
Imodeli ye-VLA ihlanganisa ukusakaza okuthathu: umbono (ozimele bekhamera), ulimi (umgomo ofana nokuthi 'faka inkomishi kusinki'), kanye nesenzo (ama-engeli ahlangene, i-gripper evulekile/yokuvaleka, noma isivinini somphumela). Google I-RT-2 ye-DeepMind yayiyingqopha-mlando: ithathe imodeli yolimi lombono eqeqeshwe ezithombeni zewebhu nombhalo, yase ilungiswa ngokuhlanganyela emigwaqweni yamarobhothi ukuze inethiwekhi efanayo iphendule ngokuthi 'sithelo sini lesi?' futhi ikhipha izenzo ezifakwe uphawu njengombhalo. Vula amamodeli afana ne-OpenVLA (amapharamitha angu-7B) kanye ne-Physical Intelligence's pi-0 elandelwayo. Okubaluleke kakhulu, lawa mamodeli abonisa ukudluliswa 'okuphuthumayo': ulwazi lwewebhu (ukubona uphawu lomkhiqizo, ukuqonda 'elincane') luholela ekukhohlisweni, ngakho irobhothi lihlanganisa izinto nemiyalelo elingakaze liyibone ngesikhathi sokuqeqeshwa kwerobhothi.
I-Technical Insight
Ama-VLA amaningi ahlukanisa izenzo eziqhubekayo zibe amathokheni ukuze isiguquli sikwazi ukubikezela ngokuzenzakalelayo, njengamagama. I-RT-2 ibeka imephu yesenzo ngasinye kowodwa wemigqomo engu-256 futhi iyikhipha njengeyunithi yezinhlamvu yombhalo. Amadizayini amasha afana ne-pi-0 anamathisela inhloko 'yochwepheshe besenzo' ehlukanisayo noma ehambisana nokugeleza kumgogodla oqandisiwe wolimi lokubona, ekhiqiza izingxenye ezibushelelezi zemvamisa ephezulu (isb., 50 Hz) esikhundleni sezinyathelo ezihlukile, ezithuthukisa ubuciko.
I-Strategic Impact
Isivinini nesikali
I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini.
Yakha ukukhetha
Amathimba aqanjiwe angakwazi ukulinganisa imiqondo ngokushesha ngezibuyekezo ezimbalwa ezenziwa mathupha.
Ithimba kanye nokusebenza komsebenzi
Imisebenzi ingasebenzisa amasiginali wesithombe nawevidiyo obekunzima ukuwenza ngaphambilini.
Ikusasa Lombono-Ulimi-Action Models for Robotics
Lindela amadathasethi amakhulu e-cross-embodiment (umzamo we-Open X-Embodiment usuvele uhlanganisa idatha evela ezinhlotsheni zamarobhothi ezingu-22+) ukuze imodeli eyodwa ishayele izingalo, ama-humanoid, nezisekelo zamaselula. Ucwaningo luphokophela ekuqondeni okusheshayo kokulawula kwesikhathi sangempela, okokufaka okucebile kwe-3D nokuthintekayo, namaketango okucabanga lapho imodeli 'icabanga' ngaphambi kokuthatha isinyathelo. Umgomo inqubomgomo eyodwa ye-generalist ongayitshela ngesiNgisi esilula, ngokulungisa usendleleni, kufana nokuxoxa nomsizi.
Ukuqaliswa Komhlaba Wangempela
I-RT-2 ilawula Google irobhothi lasekhishini ukuze 'lisuse ubhanana liye kunombolo 3' lisebenzisa amadijithi eliwafunde embhalweni wewebhu, hhayi amademo erobhothi
I-OpenVLA, imodeli ye-7B yomthombo ovulekile, elungiswe kahle ngamalebhu ukuze iqalise ukukhetha nokubeka i-tabletop ezingalweni ezibiza kancane.
I-Physical Intelligence's pi-0 ilondolo egoqayo kanye nokusula itafula ngokuhlanganisa amakhono amaningi angaphansi emyalweni owodwa.
Ingalo ye-warehouse itshele 'khetha into ebuthakathaka kakhulu' futhi isho ukuthi iyiphi into evela ekubukekeni kwayo
Izingozi & Guardrails
Amalungelo ezithombe kanye nemvume kungaba ubungozi bezomthetho uma ukuvela kungacacile.
Ukusebenza kwemodeli kungahluka kukho konke ukukhanya, izibalo zabantu, kanye nezindawo.
Okuhle okungelona iqiniso kungase kungabonakali ngaphandle uma izinga lokuzethemba liqashelwa.
Ukuqalisa Umhlahlandlela
Chaza indlela yokwamukela yokunemba, ukukhumbula, nezindleko zamaphutha.
Hlola ngedatha efana nezimo zangempela zokukhiqiza.
Engeza isibuyekezo somuntu ukuze uthole ukuzethemba okuphansi noma izibikezelo zomthelela omkhulu.
Landelela ukukhukhuleka kwemodeli bese uqinisekisa kabusha ngemva kwezinguquko zekhamera noma zesethi yedatha.
Qhubeka Uhlole
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Umhlahlandlela olandelayo
I-CLIP kanye ne-Vision-Language Models
Imibuzo evame ukubuzwa
What is Vision-Language-Action Models for Robotics?
Amamodeli we-Vision-Language-Action (VLA) amanethiwekhi amakhulu e-neural athatha izithombe zekhamera kanye nomyalelo obhaliwe kanye nemiyalo yemoto yerobhothi ephuma ngokuqondile. Zibalulekile ngoba ziletha umqondo ojwayelekile ojwayelekile wamamodeli esisekelo emishinini ebonakalayo, ivumela imodeli eyodwa ilawule irobhothi emisebenzini eminingi esikhundleni sokubhala ngesandla ukuziphatha ngakunye.
Yiziphi izinhlobo ezintathu zokufakwayo/okuphumayo ezichaza imodeli ye-Vision-Language-Action (VLA)?
I-VLA ithatha umbono (izithombe) kanye nenhloso yolimi nezenzo eziphumayo (imiyalo yezimoto), ukuqonda okuhlanganisayo, ukulandela imiyalelo, nokulawula.
Ingabe Google I-RT-2 ye-DeepMind imele kanjani izenzo zerobhothi ukuze i-transformer ikwazi ukuzikhiqiza?
I-RT-2 ibophezele ubukhulu besenzo ngasinye kumathokheni ahlukene (isb., imigqomo engu-256) futhi yawakhipha njengeyunithi yezinhlamvu, ivumela imishini yemodeli yolimi ibikezele izenzo ezifana namagama.
Kusho ukuthini 'ukudlulisa okuphuthumayo' kumongo wama-VLA?
Ngenxa yokuthi ama-VLA aqala kumamodeli olimi lombono aqeqeshwe kuwebhu, ulwazi olufana nokubona amalogo noma ukuqonda 'oluncane' ludlulisela emisebenzini yokukhohlisa engakaze ibonwe kudatha yerobhothi.
Iyiphi inzuzo eyinhloko yekhanda lesenzo lika-pi-0's diffusion/flow-matching uma kuqhathaniswa namathokheni esenzo esisodwa ahlukene?
Esikhundleni sesinyathelo esisodwa esisodwa ngesikhathi, i-pi-0 ikhiqiza izingxenye zesenzo eziqhubekayo ngemvamisa ephezulu, evumela ukunyakaza okushelelayo nokunobuciko.
Kungani i-Open X-Embodiment dataset ibalulekile kuma-VLA?
I-Open X-Embodiment ihlanganisa ama-trajectories avela kumarobhothi amaningi ahlukene, isiza izinqubomgomo zesitimela ezidlulisa izingalo, izisekelo zamaselula, nokunye ukufanisa.