NEW_AI ROBOT_MAKES_HUMAN_STUPID

Socho, woh robots jo sab kuch handle kar sakte hain — jaise cartoons mein dikhte hain. Groceries uthane se lekar, dinner banane tak, aur pet ki dekhbhal bhi karne mein madad karte hain. Yeh hi toh dream hai, right? Lekin ek twist hai. Real world mein robots ko itna saara kaam sikhana bahut mushkil hota hai, kyunki pehle robots ko har task ke liye alag training deni padti thi, aur yeh bahut time-consuming aur costly ho sakta hai. Ab, MIT ke researchers, kuch tech giants jaise Meta ke saath milke, shayad is problem ka solution dhoondh liya hai!

Yeh nayi technology ka naam hai Heterogeneous Pre-trained Transformers (HPT). Yeh system unhi large language models se inspire hai jo AI tools jaise GPT-4 ko power dete hain. Socho, alag-alag sources se data lekar, jaise simulations, real robots, aur human demo videos se, inhone ek universal robot brain banaya hai jo ek hi model se kai kaam kar sakta hai bina baar-baar retrain hone ke. Chalo, thoda aur detail mein jaate hain.

HPT: Ek Universal Robot Brain Ka Jadoo

HPT ka sabse bada fayda yeh hai ki yeh alag-alag tarah ke data ko ek system mein combine kar sakta hai. Jaise, cameras ke visuals, sensors ke signals, aur human-guided demo videos. Normal robots mein har ek ka apna setup hota hai, alag sensors aur cameras alag jagah par hote hain. Lekin HPT in saare data ko ek “shared language” mein align karta hai, taaki ek single model in sab cheezon ko samajh sake.

Toh HPT kaam kaise karta hai? Inhone alag-alag sources se data collect kiya — visual inputs, sensors, robotic arms aur unke movements — aur HPT ne inko process kiya transformers ke through, bilkul waise hi jaise GPT-4 jaise models language ko process karte hain. Bas sentences aur paragraphs ki jagah, yahan robots ke data tokens feed kiye gaye hain.

Aur iss tarah diverse data sources ko combine karke, yeh robot brain patterns aur tasks ko zyada flexible tareeke se seekh paata hai. Ab yeh hi dekhlo, jab is model ka test hua, toh HPT ne robots ke performance mein 20% improvement dikhaya! Aur yeh sirf wahi kaam nahi kar raha tha jo pehle sikhaya gaya tha — yeh naye naye tasks bhi handle kar raha tha.

Training Ka Scale: Massive Data Collection

HPT ko train karne ke liye ek massive dataset chahiye tha, jo inhone 200,000 robot trajectories aur 52 datasets se liya, jinmein human demonstrations aur simulations bhi shamil the. Yeh bahut important step tha kyunki ab tak robotics training data ka focus single tasks aur specific setups par hi hota tha. Yahan inhone sab kuch ek bade, broad model mein shamil kiya.

Ek aur challenge yeh tha ki yeh data bahut diverse tha, jaise alag-alag robot designs, environments aur tasks se aata tha. Toh isliye inhone ek universal robotic language create kiya, jo sab input ko ek hi shared language mein convert karta hai, jaise language models mein pre-training se broad understanding milti hai aur phir specific tasks pe fine-tuning hoti hai.

Ab socho, future mein agar robots sirf ek task nahi, multiple tasks kar sake, toh kya kuch ho sakta hai! Imagine karo ek robot jo pehle tumhare liye khana bana raha hai, phir wahi laundry fold kar raha hai, aur phir tumhare pet ko khana bhi de raha hai — aur usko har naye kaam ke liye retrain nahi karna pad raha. Yeh HPT model aise robots ko reality mein lane ka ek bada step ho sakta hai.

HPT Ka Setup: Stem, Trunk aur Head

HPT ke andar teen main components hain: stems, trunk, aur heads. Stem ka role hai translator ka. Yeh alag-alag input data ko ek shared language mein convert karta hai, jaise camera visuals ya sensors ka data. Trunk, jo system ka dil hai, us data ko process karta hai. Phir head specific actions mein us data ko convert karta hai. Iska fayda yeh hai ki har robot apna unique stem aur head setup rakhta hai, lekin trunk universal rehta hai.

Real-World Tests aur Results

Yeh sirf ek theoretical model nahi hai; inhone isko simulated aur real-world scenarios mein test bhi kiya. Simulations mein, jaise object ko move karna ya environment ke saath interact karna, HPT consistently outperform karta raha. Real-world robots mein bhi, jaise pet ko khilana ya assembly tasks karna, HPT zyada robust aur adaptable nikla. Inhone apne tests popular simulation platforms jaise MetaWorld aur RoboMimic par kiye.

Future Goals aur Scope

Ab unka next step hai ki yeh model long-horizon tasks ko bhi handle kar sake, jisme zyada time aur complex actions involve hote hain. Filhaal wo short-horizon tasks pe focus kar rahe hain, lekin unka goal hai ki model aur reliable aur accurate ho.

Toh ye tha HPT — ek naya, flexible robot brain jo future mein robots ko kai saare tasks handle karne mein madad kar sakta hai. Isko develop karne mein jo technical brilliance aur vision gaya hai, usse lagta hai ki hum ek aise future ki taraf badh rahe hain jahan robots zyada capable, adaptable aur thode human-like ho jayenge.

Kaun jaanta hai, ek din hum sabke paas apna apna Rosie the Robot hoga, jo humare har kaam mein madad karega!