{"id":133,"date":"2026-08-26T07:03:12","date_gmt":"2026-08-26T07:03:12","guid":{"rendered":"https:\/\/www.graveiensai.com\/blog\/?p=133"},"modified":"2026-08-26T07:03:12","modified_gmt":"2026-08-26T07:03:12","slug":"physical-ai-robotics-training-data","status":"publish","type":"post","link":"https:\/\/www.graveiensai.com\/blog\/physical-ai-robotics-training-data\/","title":{"rendered":"Physical AI Needs a Body of Evidence: The 2026 Guide to Robotics Training Data"},"content":{"rendered":"\n

Phys\u00adi\u00adcal AI is arti\u00adfi\u00adcial intel\u00adli\u00adgence that per\u00adceives, rea\u00adsons, and acts in the real world through robots and machines, and it learns almost entire\u00adly from robot\u00adics train\u00ading data: the real, sim\u00adu\u00adlat\u00aded, and human demon\u00adstra\u00adtion data that teach\u00ades a machine how to move, grasp, and com\u00adplete tasks. In oth\u00ader words, a robot brain is only as capa\u00adble as the exam\u00adples it has seen. This guide explains what phys\u00adi\u00adcal AI is, why the data behind it mat\u00adters more than the mod\u00adel, the main types of robot\u00adics train\u00ading data and when each one wins, the bench\u00admarks and datasets that define the field in 2026, and a prac\u00adti\u00adcal, repeat\u00adable way to plan your own data mix.<\/p>\n\n\n\n

If you are decid\u00ading how to col\u00adlect or buy data for a robot, an autonomous sys\u00adtem, or an embod\u00adied agent, read the At a glance answers first, then use the com\u00adpar\u00adi\u00adson table and the Data Readi\u00adness Matrix fur\u00adther down to make the call.<\/p>\n\n\n\n

At a glance: physical AI and robotics training data<\/strong><\/h2>\n\n\n\n
Ques\u00adtion<\/strong><\/th>Short answer<\/strong><\/th><\/tr><\/thead>
What is phys\u00adi\u00adcal AI?<\/td>AI that oper\u00adates in and inter\u00adacts with the phys\u00adi\u00adcal world through sen\u00adsors and actu\u00ada\u00adtors, not just soft\u00adware, accord\u00ading to IBM\u2019s def\u00adi\u00adn\u00adi\u00adtion.<\/td><\/tr>
What is robot\u00adics train\u00ading data?<\/td>The demon\u00adstra\u00adtions, sen\u00adsor logs, and sim\u00adu\u00adlat\u00aded sce\u00adnar\u00adios that teach a robot pol\u00adi\u00adcy how to per\u00adceive and act. It is the fuel for phys\u00adi\u00adcal AI.<\/td><\/tr>
Why does the data mat\u00adter so much?<\/td>Mod\u00adel archi\u00adtec\u00adtures are increas\u00ading\u00adly shared and open. For many teams, the qual\u00adi\u00adty, diver\u00adsi\u00adty, and real\u00adism of robot\u00adics train\u00ading data is the decid\u00ading fac\u00adtor in whether a robot gen\u00ader\u00adal\u00adizes.<\/td><\/tr>
What are the main data types?<\/td>Real-world and ego\u00adcen\u00adtric cap\u00adture, human tele\u00adop\u00ader\u00ada\u00adtion, sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic data, world mod\u00adels, and cross-embod\u00adi\u00adment web-scale datasets.<\/td><\/tr>
Who pop\u00adu\u00adlar\u00adized the term?<\/td>Nvidia CEO Jensen Huang is wide\u00adly cred\u00adit\u00aded with pop\u00adu\u00adlar\u00adiz\u00ading phys\u00adi\u00adcal AI, which he called the next Chat\u00adG\u00adPT moment at CES 2026.<\/td><\/tr>
How should I choose a data mix?<\/td>Match the data source to task dex\u00adter\u00adi\u00adty and envi\u00adron\u00adment diver\u00adsi\u00adty. Most pro\u00adduc\u00adtion sys\u00adtems blend sim\u00adu\u00adla\u00adtion for scale with real human demon\u00adstra\u00adtions for real\u00adism.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Table of contents<\/strong><\/h2>\n\n\n\n

\u2022  What is phys\u00adi\u00adcal AI?<\/p>\n\n\n\n

\u2022  Why robot\u00adics train\u00ading data decides whether phys\u00adi\u00adcal AI works<\/p>\n\n\n\n

\u2022  The types of robot\u00adics train\u00ading data<\/p>\n\n\n\n

\u2022  Com\u00adpar\u00adi\u00adson table: robot\u00adics train\u00ading data sources<\/p>\n\n\n\n

\u2022  When each data source wins: a nuanced view<\/p>\n\n\n\n

\u2022  World mod\u00adels AI and the syn\u00adthet\u00adic data ques\u00adtion<\/p>\n\n\n\n

\u2022  Embod\u00adied AI datasets and bench\u00admarks that mat\u00adter in 2026<\/p>\n\n\n\n

\u2022  The Graveiens Data Readi\u00adness Matrix<\/p>\n\n\n\n

\u2022  How to build a robot\u00adics train\u00ading data pipeline: a check\u00adlist<\/p>\n\n\n\n

\u2022  A worked exam\u00adple: from brit\u00adtle demo to reli\u00adable pol\u00adi\u00adcy<\/p>\n\n\n\n

\u2022  How high-qual\u00adi\u00adty phys\u00adi\u00adcal AI data is actu\u00adal\u00adly built<\/p>\n\n\n\n

\u2022  Fre\u00adquent\u00adly asked ques\u00adtions<\/p>\n\n\n\n

\u2022  About the authors<\/p>\n\n\n\n

\u2022  Con\u00adclu\u00adsion<\/p>\n\n\n\n

\u2022  Sources<\/p>\n\n\n\n

What is physical AI?<\/strong><\/h2>\n\n\n\n

Phys\u00adi\u00adcal AI is arti\u00adfi\u00adcial intel\u00adli\u00adgence that sens\u00ades, under\u00adstands, and acts in the real world, clos\u00ading the loop between per\u00adcep\u00adtion and action through robots, vehi\u00adcles, and machines. IBM defines it as \u201carti\u00adfi\u00adcial intel\u00adli\u00adgence (AI) sys\u00adtems that oper\u00adate in and inter\u00adact with the phys\u00adi\u00adcal world, rather than exist\u00ading only in soft\u00adware or dig\u00adi\u00adtal envi\u00adron\u00adments,\u201d<\/em> com\u00adbin\u00ading AI mod\u00adels with \u201csen\u00adsors, actu\u00ada\u00adtors, and oth\u00ader con\u00adtrol sys\u00adtems that allow mod\u00adels to act upon real-world envi\u00adron\u00adments.\u201d<\/em><\/p>\n\n\n\n

The con\u00adtrast with gen\u00ader\u00ada\u00adtive AI is use\u00adful. A chat\u00adbot pre\u00addicts the next token in a sen\u00adtence, and a mis\u00adtake pro\u00adduces an awk\u00adward para\u00adgraph. A phys\u00adi\u00adcal AI sys\u00adtem pre\u00addicts the next action for a body with mass and momen\u00adtum, and a mis\u00adtake can knock a cup off a table or stall a ware\u00adhouse line. That is why phys\u00adi\u00adcal AI depends on ground\u00aded, phys\u00adi\u00adcal\u00adly accu\u00adrate exam\u00adples rather than text scraped from the open web.<\/p>\n\n\n\n

Gen\u00ader\u00ada\u00adtive mod\u00adels are not the rival here. They are the enabler. Foun\u00adda\u00adtion mod\u00adels gave robots a com\u00admon sense start\u00ading point about objects and lan\u00adguage, and the cur\u00adrent wave of vision-lan\u00adguage-action (VLA) mod\u00adels extends that rea\u00adson\u00ading into motor con\u00adtrol. The miss\u00ading ingre\u00addi\u00adent, and the hard part, is the robot\u00adics train\u00ading data that maps what a robot sees to what it should do.<\/p>\n\n\n\n

Inter\u00adest in the field accel\u00ader\u00adat\u00aded sharply in ear\u00adly 2026. Nvidia CEO Jensen Huang, who is wide\u00adly cred\u00adit\u00aded with pop\u00adu\u00adlar\u00adiz\u00ading the term, told audi\u00adences around CES 2026 that a Chat\u00adG\u00adPT moment for phys\u00adi\u00adcal AI was com\u00ading and spoke of a future with a bil\u00adlion robots. Whether or not that num\u00adber lands, the direc\u00adtion is clear: the race in robot\u00adics has shift\u00aded from build\u00ading big\u00adger mod\u00adels to build\u00ading bet\u00adter data.<\/p>\n\n\n\n

Why robotics training data decides whether physical AI works<\/strong><\/h2>\n\n\n\n

For many robot\u00adics teams, robot\u00adics train\u00ading data is now the pri\u00adma\u00adry bot\u00adtle\u00adneck, not mod\u00adel size or com\u00adpute. Lan\u00adguage mod\u00adels had the entire inter\u00adnet to learn from. Robots have no equiv\u00ada\u00adlent cor\u00adpus of phys\u00adi\u00adcal expe\u00adri\u00adence, so the data has to be cre\u00adat\u00aded delib\u00ader\u00adate\u00adly, action by action.<\/p>\n\n\n\n

Three prop\u00ader\u00adties sep\u00ada\u00adrate data that pro\u00adduces a reli\u00adable robot from data that pro\u00adduces an impres\u00adsive demo. Cov\u00ader\u00adage comes first: a pol\u00adi\u00adcy trained only on tidy lab kitchens fails in a clut\u00adtered real one, so diver\u00adsi\u00adty of envi\u00adron\u00adments, objects, and edge cas\u00ades is what lets a phys\u00adi\u00adcal AI mod\u00adel gen\u00ader\u00adal\u00adize instead of mem\u00ado\u00adrize. Fideli\u00adty comes sec\u00adond: phys\u00adi\u00adcal AI has to respect fric\u00adtion, con\u00adtact forces, and tim\u00ading, and data that is slight\u00adly wrong about physics teach\u00ades habits that break on real hard\u00adware, the gap prac\u00adti\u00adtion\u00aders call the sim-to-real prob\u00adlem. Prove\u00adnance comes third, and it is increas\u00ading\u00adly a legal require\u00adment rather than a nice\u00adty: human demon\u00adstra\u00adtion and ego\u00adcen\u00adtric video cap\u00adture real peo\u00adple and real spaces, so con\u00adsent, licens\u00ading, and a clear audit trail decide whether you can actu\u00adal\u00adly deploy a mod\u00adel trained on that data. Qual\u00adi\u00adty con\u00adtrol mat\u00adters as much as quan\u00adti\u00adty, which is why seri\u00adous pro\u00adgrams pair col\u00adlec\u00adtion with rig\u00ador\u00adous data anno\u00adta\u00adtion and val\u00adi\u00adda\u00adtion<\/a> before any\u00adthing reach\u00ades a train\u00ading run.<\/p>\n\n\n\n

The types of robotics training data<\/strong><\/h2>\n\n\n\n

There are five main types of robot\u00adics train\u00ading data, and mod\u00adern phys\u00adi\u00adcal AI sys\u00adtems almost always com\u00adbine sev\u00ader\u00adal rather than bet\u00adting on one. Each answers a dif\u00adfer\u00adent ques\u00adtion about how an embod\u00adied AI sys\u00adtem should behave.<\/p>\n\n\n\n

Real-world and egocentric capture<\/strong><\/h3>\n\n\n\n

This is human first-per\u00adson video and sen\u00adsor data record\u00aded as peo\u00adple do real tasks, often through head-mount\u00aded cam\u00aderas that see the world rough\u00adly the way a robot\u00ad\u2019s sen\u00adsors would. Ego\u00adcen\u00adtric cap\u00adture is prized for embod\u00adied AI because it records nat\u00adur\u00adal hand-object inter\u00adac\u00adtion, gaze, and the exact sequence of task steps in messy real set\u00adtings. Pro\u00adgrams like struc\u00adtured ego\u00adcen\u00adtric video data col\u00adlec\u00adtion<\/a> gath\u00ader this first-per\u00adson footage across kitchens, ware\u00adhous\u00ades, and work\u00adshops with con\u00adsent built in, which is what makes it usable for imi\u00adta\u00adtion learn\u00ading and VLA train\u00ading.<\/p>\n\n\n\n

Human teleoperation<\/strong><\/h3>\n\n\n\n

Here a per\u00adson remote\u00adly oper\u00adates the actu\u00adal robot, and every joint move\u00adment is record\u00aded as a demon\u00adstra\u00adtion on the tar\u00adget hard\u00adware. Tele\u00adop\u00ader\u00ada\u00adtion pro\u00adduces the clean\u00adest pos\u00adsi\u00adble action labels because the data lives in the robot\u00ad\u2019s own body, which is why it anchors many manip\u00adu\u00adla\u00adtion datasets. It is slow\u00ader and more expen\u00adsive per hour than video, but the qual\u00adi\u00adty is hard to beat. Our deep\u00ader explain\u00ader on how tele\u00adop\u00ader\u00ada\u00adtion teach\u00ades robots real work<\/a> walks through the trade\u00adoffs.<\/p>\n\n\n\n

Simulation and synthetic data<\/strong><\/h3>\n\n\n\n

Physics sim\u00adu\u00adla\u00adtors gen\u00ader\u00adate labeled sce\u00adnar\u00adios at mas\u00adsive scale, run\u00adning thou\u00adsands of vir\u00adtu\u00adal robots in par\u00adal\u00adlel to prac\u00adtice a skill mil\u00adlions of times. Sim\u00adu\u00adla\u00adtion is cheap, safe, and infi\u00adnite\u00adly repeat\u00adable, and it is excel\u00adlent for teach\u00ading embod\u00adied AI loco\u00admo\u00adtion, nav\u00adi\u00adga\u00adtion, and rein\u00adforce\u00adment learn\u00ading. Its weak\u00adness is the real\u00adi\u00adty gap: sub\u00adtle dif\u00adfer\u00adences between sim\u00adu\u00adlat\u00aded and real physics, tex\u00adtures, and sen\u00adsor noise mean sim-trained poli\u00adcies usu\u00adal\u00adly need real data to fine-tune.<\/p>\n\n\n\n

World models<\/strong><\/h3>\n\n\n\n

A world mod\u00adel is an AI sys\u00adtem that has learned the dynam\u00adics of the phys\u00adi\u00adcal world, includ\u00ading geom\u00ade\u00adtry, motion, and physics, from large amounts of real data, and can then gen\u00ader\u00adate real\u00adis\u00adtic, physics-aware sce\u00adnar\u00adios on demand. World mod\u00adels blur the line between sim\u00adu\u00adla\u00adtion and real\u00adi\u00adty by pro\u00adduc\u00ading syn\u00adthet\u00adic yet believ\u00adable video and inter\u00adac\u00adtions to train and eval\u00adu\u00adate robots.<\/p>\n\n\n\n

Cross-embodiment and web-scale datasets<\/strong><\/h3>\n\n\n\n

These are large pooled datasets that com\u00adbine demon\u00adstra\u00adtions from many dif\u00adfer\u00adent robots and labs so a sin\u00adgle pol\u00adi\u00adcy can learn trans\u00adfer\u00adable skills. They give an embod\u00adied AI mod\u00adel breadth it could nev\u00ader get from one robot in one build\u00ading, and they under\u00adpin the recent gen\u00ader\u00ada\u00adtion of gen\u00ader\u00adal-pur\u00adpose robot foun\u00adda\u00adtion mod\u00adels.<\/p>\n\n\n\n

Comparison table: robotics training data sources<\/strong><\/h2>\n\n\n\n

The table below com\u00adpares the main robot\u00adics train\u00ading data sources across the fac\u00adtors that actu\u00adal\u00adly dri\u00adve a phys\u00adi\u00adcal AI build-or-buy deci\u00adsion. Use it as a quick ref\u00ader\u00adence, then read the nuanced sec\u00adtion that fol\u00adlows, because the right answer is almost always a blend.<\/p>\n\n\n\n

Data source<\/strong><\/th>Action-label fideli\u00adty<\/strong><\/th>Scale and cost<\/strong><\/th>Real-world real\u00adism<\/strong><\/th>Best for<\/strong><\/th>Main lim\u00adi\u00adta\u00adtion<\/strong><\/th><\/tr><\/thead>
Real-world ego\u00adcen\u00adtric video<\/strong><\/td>Medi\u00adum (needs retar\u00adget\u00ading)<\/td>High vol\u00adume, mod\u00ader\u00adate cost<\/td>Very high<\/td>Imi\u00adta\u00adtion learn\u00ading, VLA mod\u00adels, task under\u00adstand\u00ading<\/td>Human body dif\u00adfers from robot body<\/td><\/tr>
Human tele\u00adop\u00ader\u00ada\u00adtion<\/strong><\/td>Very high (native robot actions)<\/td>Low vol\u00adume, high cost per hour<\/td>High<\/td>Pre\u00adcise manip\u00adu\u00adla\u00adtion, dex\u00adter\u00adous grasp\u00ading<\/td>Slow and expen\u00adsive to scale<\/td><\/tr>
Sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic<\/strong><\/td>High with\u00adin the sim\u00adu\u00adla\u00adtor<\/td>Very high vol\u00adume, very low cost<\/td>Medi\u00adum (real\u00adi\u00adty gap)<\/td>Loco\u00admo\u00adtion, nav\u00adi\u00adga\u00adtion, rein\u00adforce\u00adment learn\u00ading<\/td>Sim-to-real trans\u00adfer required<\/td><\/tr>
World mod\u00adels AI<\/strong><\/td>High and con\u00adtrol\u00adlable<\/td>High vol\u00adume, mod\u00ader\u00adate cost<\/td>High and improv\u00ading<\/td>Sce\u00adnario gen\u00ader\u00ada\u00adtion, pol\u00adi\u00adcy eval\u00adu\u00ada\u00adtion, rare edge cas\u00ades<\/td>New\u00ader, still matur\u00ading in 2026<\/td><\/tr>
Cross-embod\u00adi\u00adment datasets<\/strong><\/td>Mixed (varies by con\u00adtrib\u00adu\u00adtor)<\/td>Very high vol\u00adume, shared cost<\/td>High but het\u00adero\u00adge\u00adneous<\/td>Gen\u00ader\u00adal skills, foun\u00adda\u00adtion mod\u00adel pre\u00adtrain\u00ading<\/td>Incon\u00adsis\u00adtent for\u00admats and qual\u00adi\u00adty<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

An HTML ver\u00adsion of this com\u00adpar\u00adi\u00adson table is includ\u00aded in the SEO pack\u00adage so it ren\u00adders clean\u00adly on pub\u00adlish.<\/em><\/p>\n\n\n\n

When each data source wins: a nuanced view<\/strong><\/h2>\n\n\n\n

No sin\u00adgle source is best, and any ven\u00addor who tells you oth\u00ader\u00adwise is sell\u00ading one thing. Each data type is strongest for a spe\u00adcif\u00adic job, and the lead\u00ading phys\u00adi\u00adcal AI pro\u00adgrams delib\u00ader\u00adate\u00adly lay\u00ader them.<\/p>\n\n\n\n

Sim\u00adu\u00adla\u00adtion wins when the skill is about dynam\u00adics and rep\u00ade\u00adti\u00adtion rather than fine con\u00adtact: walk\u00ading, bal\u00adanc\u00ading, drone flight, and nav\u00adi\u00adga\u00adtion. You can prac\u00adtice a bil\u00adlion steps overnight, and the physics of a falling body is well under\u00adstood, so the real\u00adi\u00adty gap is man\u00adage\u00adable. It strug\u00adgles with rich con\u00adtact tasks like fold\u00ading cloth or plug\u00adging in a cable, where real fric\u00adtion and defor\u00adma\u00adtion are hard to mod\u00adel.<\/p>\n\n\n\n

Tele\u00adop\u00ader\u00ada\u00adtion wins when you need pre\u00adci\u00adsion on the exact robot you will deploy, because the action labels are native and unam\u00adbigu\u00adous. It suits a nar\u00adrow, high-val\u00adue manip\u00adu\u00adla\u00adtion skill, and it is the wrong choice when you need thou\u00adsands of hours of vari\u00adety, because the cost curve is bru\u00adtal.<\/p>\n\n\n\n

Ego\u00adcen\u00adtric and real-world cap\u00adture wins when the goal is gen\u00ader\u00adal\u00adiza\u00adtion to human envi\u00adron\u00adments. First-per\u00adson human video is far cheap\u00ader to gath\u00ader at scale than tele\u00adop\u00ader\u00ada\u00adtion and cap\u00adtures the long tail of real objects and clut\u00adter, which is exact\u00adly what embod\u00adied AI needs to leave the lab. The trade\u00adoff is that a human hand is not a robot grip\u00adper, so the data must be retar\u00adget\u00aded, and qual\u00adi\u00adty con\u00adtrol is essen\u00adtial.<\/p>\n\n\n\n

World mod\u00adels AI sits in between, and its role is grow\u00ading fastest, let\u00adting teams gen\u00ader\u00adate rare or dan\u00adger\u00adous sce\u00adnar\u00adios on demand and eval\u00adu\u00adate embod\u00adied AI poli\u00adcies in a con\u00adtrol\u00adlable syn\u00adthet\u00adic world before touch\u00ading hard\u00adware.<\/p>\n\n\n\n

In prac\u00adtice the hybrid pat\u00adtern dom\u00adi\u00adnates: pre\u00adtrain broad\u00adly on cross-embod\u00adi\u00adment and sim\u00adu\u00adlat\u00aded data for scale, then fine-tune on curat\u00aded real human demon\u00adstra\u00adtions and ego\u00adcen\u00adtric video for real\u00adism. The blend, not the sin\u00adgle source, is the strat\u00ade\u00adgy.<\/p>\n\n\n\n

World models AI and the synthetic data question<\/strong><\/h2>\n\n\n\n

World mod\u00adels AI is the fastest-mov\u00ading fron\u00adtier in robot\u00adics train\u00ading data because it promis\u00ades the scale of sim\u00adu\u00adla\u00adtion with some\u00adthing much clos\u00ader to the real\u00adism of the real world. Instead of hand-build\u00ading a sim\u00adu\u00adla\u00adtor, a world foun\u00adda\u00adtion mod\u00adel learns physics from real footage and then gen\u00ader\u00adates new, phys\u00adi\u00adcal\u00adly plau\u00adsi\u00adble sce\u00adnar\u00adios that a robot can learn from or be test\u00aded against.<\/p>\n\n\n\n

The clear\u00adest exam\u00adple in 2026 is Nvidi\u00ada\u2019s Cos\u00admos fam\u00adi\u00adly. At CES 2026, Nvidia released Cos\u00admos Trans\u00adfer 2.5 and Cos\u00admos Pre\u00addict 2.5, which the com\u00adpa\u00adny describes as \u201copen, ful\u00adly cus\u00adtomiz\u00adable world mod\u00adels that enable phys\u00adi\u00adcal\u00adly based syn\u00adthet\u00adic data gen\u00ader\u00ada\u00adtion and robot pol\u00adi\u00adcy eval\u00adu\u00ada\u00adtion in sim\u00adu\u00adla\u00adtion,\u201d<\/em> along\u00adside Cos\u00admos Rea\u00adson 2, a rea\u00adson\u00ading vision lan\u00adguage mod\u00adel built to help machines see, under\u00adstand, and act in the phys\u00adi\u00adcal world like humans. The pitch is straight\u00adfor\u00adward: use world mod\u00adels AI to mul\u00adti\u00adply a small amount of expen\u00adsive real data into a large, var\u00adied train\u00ading set.<\/p>\n\n\n\n

The cau\u00adtion is equal\u00adly impor\u00adtant. Syn\u00adthet\u00adic data ampli\u00adfies what\u00adev\u00ader assump\u00adtions are baked into the gen\u00ader\u00ada\u00adtor, so a world mod\u00adel trained on biased or thin real data will con\u00adfi\u00addent\u00adly pro\u00adduce biased or thin sce\u00adnar\u00adios. That is why world mod\u00adels AI does not remove the need for high-qual\u00adi\u00adty real cap\u00adture; it rais\u00ades the val\u00adue of it. The best real data becomes the seed that makes world mod\u00adels AI trust\u00adwor\u00adthy, and the best real data is still human demon\u00adstra\u00adtion and ego\u00adcen\u00adtric cap\u00adture.<\/p>\n\n\n\n

Embodied AI datasets and benchmarks that matter in 2026<\/strong><\/h2>\n\n\n\n

Embod\u00adied AI is AI that learns through a phys\u00adi\u00adcal or sim\u00adu\u00adlat\u00aded body by inter\u00adact\u00ading with an envi\u00adron\u00adment, and a hand\u00adful of open datasets have shaped how the field trains and mea\u00adsures progress. Know\u00ading them is the fastest way to under\u00adstand what good robot\u00adics train\u00ading data looks like.<\/p>\n\n\n\n

Ego4D, released by a Meta-led con\u00adsor\u00adtium, con\u00adtributed rough\u00adly 3,670 hours of ego\u00adcen\u00adtric video and set the tem\u00adplate for first-per\u00adson data at scale. Its suc\u00adces\u00adsor, Ego-Exo4D, announced by Meta\u2019s FAIR lab, Project Aria, and 15 uni\u00adver\u00adsi\u00adty part\u00adners in late 2023, added syn\u00adchro\u00adnized first-per\u00adson and third-per\u00adson views, gath\u00ader\u00ading more than 1,400 hours of video from over 800 par\u00adtic\u00adi\u00adpants across six coun\u00adtries. These datasets are why ego\u00adcen\u00adtric cap\u00adture is now cen\u00adtral to embod\u00adied AI research.<\/p>\n\n\n\n

On the robot side, Open X\u2011Embodiment, coor\u00addi\u00adnat\u00aded by Google Deep\u00adMind with dozens of labs, pooled more than one mil\u00adlion real robot tra\u00adjec\u00adto\u00adries span\u00adning 22 robot embod\u00adi\u00adments and 527 skills, and it was used to train the RT-1\u2011X and RT-2\u2011X mod\u00adels that demon\u00adstrat\u00aded skill trans\u00adfer across dif\u00adfer\u00adent robots. DROID, pub\u00adlished in 2024, added 76,000 demon\u00adstra\u00adtion tra\u00adjec\u00adto\u00adries, about 350 hours of inter\u00adac\u00adtion across 564 scenes and 86 tasks, col\u00adlect\u00aded by 13 insti\u00adtu\u00adtions across three con\u00adti\u00adnents.<\/p>\n\n\n\n

The mod\u00adel side moved just as fast. Phys\u00adi\u00adcal Intel\u00adli\u00adgence intro\u00adduced its pi0 (pi-zero) vision-lan\u00adguage-action flow mod\u00adel for gen\u00ader\u00adal robot con\u00adtrol in late 2024, and at CES 2026 Nvidia released Isaac GR00T N1.6, an open vision-lan\u00adguage-action mod\u00adel pur\u00adpose-built for humanoid robots. The com\u00admon thread across all of these embod\u00adied AI mile\u00adstones is unmis\u00adtak\u00adable: the break\u00adthroughs track the data. Teams that want to com\u00adpete on phys\u00adi\u00adcal AI com\u00adpete first on the qual\u00adi\u00adty of their data col\u00adlec\u00adtion<\/a> and label\u00ading, and often on sen\u00adsor-rich inputs like sen\u00adsor fusion and LiDAR<\/a> for per\u00adcep\u00adtion.<\/p>\n\n\n\n

If you are new to how raw sen\u00adsor input becomes usable labels, our guide to object detec\u00adtion for com\u00adput\u00ader vision teams<\/a> is a help\u00adful primer, and instruc\u00adtion tun\u00ading<\/a> explains how VLA and lan\u00adguage mod\u00adels are adapt\u00aded to fol\u00adlow task com\u00admands.<\/p>\n\n\n\n

The Graveiens Data Readiness Matrix<\/strong><\/h2>\n\n\n\n

To turn all of this into a deci\u00adsion you can actu\u00adal\u00adly make, we use a sim\u00adple, repeat\u00adable tool we call the Graveiens Data Readi\u00adness Matrix. It maps two ques\u00adtions that most reli\u00adably pre\u00addict which robot\u00adics train\u00ading data mix you need: how dex\u00adter\u00adous the task is (how much fine con\u00adtact and manip\u00adu\u00adla\u00adtion it requires) and how diverse the deploy\u00adment envi\u00adron\u00adment is (how var\u00adied the objects, light\u00ading, and set\u00adtings will be). Score each from low to high, then read your quad\u00adrant.<\/p>\n\n\n\n

<\/th>Low envi\u00adron\u00adment diver\u00adsi\u00adty<\/strong><\/th>High envi\u00adron\u00adment diver\u00adsi\u00adty<\/strong><\/th><\/tr><\/thead>
Low task dex\u00adter\u00adi\u00adty<\/strong><\/td>Quad\u00adrant 1: Sim\u00adu\u00adla\u00adtion-first. Lean on syn\u00adthet\u00adic and sim\u00adu\u00adlat\u00aded data, with light real val\u00adi\u00adda\u00adtion. Exam\u00adple: a deliv\u00adery robot nav\u00adi\u00adgat\u00ading a fixed ware\u00adhouse loop.<\/td>Quad\u00adrant 2: Sim\u00adu\u00adla\u00adtion plus ego\u00adcen\u00adtric. Pre\u00adtrain in sim\u00adu\u00adla\u00adtion, then add real ego\u00adcen\u00adtric video for the long tail. Exam\u00adple: an indoor robot nav\u00adi\u00adgat\u00ading many dif\u00adfer\u00adent homes.<\/td><\/tr>
High task dex\u00adter\u00adi\u00adty<\/strong><\/td>Quad\u00adrant 3: Tele\u00adop\u00ader\u00ada\u00adtion-first. Invest in native robot demon\u00adstra\u00adtions for pre\u00adci\u00adsion, add sim\u00adu\u00adla\u00adtion for edge cas\u00ades. Exam\u00adple: a fixed-sta\u00adtion assem\u00adbly arm doing one exact\u00ading inser\u00adtion.<\/td>Quad\u00adrant 4: Full blend. Com\u00adbine ego\u00adcen\u00adtric cap\u00adture, tele\u00adop\u00ader\u00ada\u00adtion, sim\u00adu\u00adla\u00adtion, and world mod\u00adels AI. Exam\u00adple: a gen\u00ader\u00adal-pur\u00adpose humanoid work\u00ading across kitchens and work\u00adshops.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The matrix is delib\u00ader\u00adate\u00adly not a rank\u00ading of sources. It is a way to see that a ware\u00adhouse nav\u00adi\u00adga\u00adtion robot and a humanoid fold\u00ading laun\u00addry live in dif\u00adfer\u00adent quad\u00adrants and there\u00adfore need dif\u00adfer\u00adent data strate\u00adgies. Most ambi\u00adtious phys\u00adi\u00adcal AI and embod\u00adied AI prod\u00aducts drift toward Quad\u00adrant 4 over time, which is why a mixed, well-gov\u00aderned data sup\u00adply chain beats any sin\u00adgle tech\u00adnique.<\/p>\n\n\n\n

How to build a robotics training data pipeline: a checklist <\/strong><\/h2>\n\n\n\n

Use this num\u00adbered check\u00adlist to move from idea to a train\u00ading-ready dataset with\u00adout expen\u00adsive rework.<\/p>\n\n\n\n

1.  <\/strong>Define the tar\u00adget embod\u00adi\u00adment and task.<\/strong> Write down the exact robot, grip\u00adper, and the spe\u00adcif\u00adic skill, because the hard\u00adware deter\u00admines what data is even use\u00adful.<\/p>\n\n\n\n

2.  <\/strong>Locate your quad\u00adrant.<\/strong> Use the Data Readi\u00adness Matrix above to pick a pri\u00adma\u00adry data source and a sec\u00adondary source before you spend a rupee or a dol\u00adlar on col\u00adlec\u00adtion.<\/p>\n\n\n\n

3.  <\/strong>Draft a data schema.<\/strong> Decide the modal\u00adi\u00adties (video, depth, joint states, force, lan\u00adguage instruc\u00adtions) and the exact label for\u00admat up front, so con\u00adtrib\u00adu\u00adtors and anno\u00adta\u00adtors stay con\u00adsis\u00adtent.<\/p>\n\n\n\n

4.  <\/strong>Secure con\u00adsent and licens\u00ading first.<\/strong> For any human or ego\u00adcen\u00adtric cap\u00adture, lock down par\u00adtic\u00adi\u00adpant con\u00adsent, bystander pro\u00adto\u00adcols, and a per-file audit trail before col\u00adlec\u00adtion, not after.<\/p>\n\n\n\n

5.  <\/strong>Pilot small, then scale.<\/strong> Col\u00adlect a small batch, train a quick base\u00adline, and inspect fail\u00adures. Real errors tell you what data you are miss\u00ading far bet\u00adter than a spread\u00adsheet plan.<\/p>\n\n\n\n

6.  <\/strong>Lay\u00ader in diver\u00adsi\u00adty on pur\u00adpose.<\/strong> Add envi\u00adron\u00adments, light\u00ading, object vari\u00adety, and edge cas\u00ades delib\u00ader\u00adate\u00adly, because cov\u00ader\u00adage is what turns a demo into a deploy\u00adable pol\u00adi\u00adcy.<\/p>\n\n\n\n

7.  <\/strong>Anno\u00adtate and val\u00adi\u00addate rig\u00ador\u00adous\u00adly.<\/strong> Put every clip through mul\u00adti-stage qual\u00adi\u00adty assur\u00adance, since a small per\u00adcent\u00adage of mis\u00adla\u00adbeled actions can qui\u00adet\u00adly poi\u00adson a pol\u00adi\u00adcy.<\/p>\n\n\n\n

8.  <\/strong>Blend real and syn\u00adthet\u00adic.<\/strong> Use sim\u00adu\u00adla\u00adtion and world mod\u00adels to expand rare sce\u00adnar\u00adios, but keep a curat\u00aded core of real demon\u00adstra\u00adtions as your ground truth.<\/p>\n\n\n\n

9.  <\/strong>Mea\u00adsure sim-to-real, then close the loop.<\/strong> Test on hard\u00adware, log the gaps, and feed those fail\u00adure cas\u00ades back into the next col\u00adlec\u00adtion round.<\/p>\n\n\n\n

A worked example: from brittle demo to reliable policy<\/strong><\/h2>\n\n\n\n

Con\u00adsid\u00ader a team build\u00ading a robot to sort and pack mixed items in a ful\u00adfill\u00adment cen\u00adter.<\/p>\n\n\n\n

Before: <\/strong>they trained pure\u00adly in sim\u00adu\u00adla\u00adtion. In the lab the robot hit 95 per\u00adcent suc\u00adcess on clean, uni\u00adform box\u00ades. On the real line it dropped to rough\u00adly half, fum\u00adbling shiny pack\u00adag\u00ading, unusu\u00adal shapes, and items the sim\u00adu\u00adla\u00adtor nev\u00ader ren\u00addered. The pol\u00adi\u00adcy had mem\u00ado\u00adrized a tidy world that did not exist.<\/p>\n\n\n\n

After: <\/strong>the team re-planned using the Data Readi\u00adness Matrix and land\u00aded in Quad\u00adrant 4. They kept sim\u00adu\u00adla\u00adtion for reach-and-place motion, added a few hun\u00addred hours of ego\u00adcen\u00adtric video of real ware\u00adhouse work\u00aders pack\u00ading var\u00adied items, and used tele\u00adop\u00ader\u00ada\u00adtion for the twen\u00adty hard\u00adest grasps. World mod\u00adels AI filled in rare cas\u00ades like torn pack\u00adag\u00ading. After retrain\u00ading, the same robot gen\u00ader\u00adal\u00adized to the messy real line because its data final\u00adly matched real\u00adi\u00adty. The mod\u00adel did not change much. The data did.<\/p>\n\n\n\n

The les\u00adson repeats across the indus\u00adtry: when a phys\u00adi\u00adcal AI sys\u00adtem under\u00adper\u00adforms, the fix is usu\u00adal\u00adly bet\u00adter, more rep\u00adre\u00adsen\u00adta\u00adtive robot\u00adics train\u00ading data, not a big\u00adger net\u00adwork. In phys\u00adi\u00adcal AI, data is the dif\u00adfer\u00aden\u00adtia\u00adtor.<\/p>\n\n\n\n

How high-quality physical AI data is actually built<\/strong><\/h2>\n\n\n\n

Great phys\u00adi\u00adcal AI data is not scraped, it is pro\u00adduced, and the pro\u00adduc\u00adtion qual\u00adi\u00adty is what sep\u00ada\u00adrates robot\u00adics train\u00ading data you can deploy from a dataset you can\u00adnot. The hard parts are rarely tech\u00adni\u00adcal alone. They are oper\u00ada\u00adtional: recruit\u00ading diverse real par\u00adtic\u00adi\u00adpants, dis\u00adtrib\u00adut\u00ading and retriev\u00ading cap\u00adture devices, obtain\u00ading air\u00adtight con\u00adsent, and run\u00adning enough qual\u00adi\u00adty assur\u00adance pass\u00ades that the labels can be trust\u00aded at scale.<\/p>\n\n\n\n

This is the work Graveiens AI spe\u00adcial\u00adizes in as a human-in-the-loop, ISO 9001:2017 cer\u00adti\u00adfied data part\u00adner. Our ego\u00adcen\u00adtric pro\u00adgrams use head-mount\u00aded rigs to cap\u00adture con\u00adsent\u00aded first-per\u00adson footage across real Indi\u00adan envi\u00adron\u00adments, includ\u00ading tier\u20112 and tier\u20113 set\u00adtings that most datasets nev\u00ader reach, with a per-file audit trail and mul\u00adti-stage QA before deliv\u00adery. That diver\u00adsi\u00adty is pre\u00adcise\u00adly what embod\u00adied AI mod\u00adels need to gen\u00ader\u00adal\u00adize beyond a hand\u00adful of coastal labs.<\/p>\n\n\n\n

Teams build\u00ading per\u00adcep\u00adtion for vehi\u00adcles and robots often pair this with ded\u00adi\u00adcat\u00aded com\u00adput\u00ader vision<\/a> label\u00ading and ADAS and autonomous<\/a> datasets, and adapt lan\u00adguage and rea\u00adson\u00ading behav\u00adior through gen\u00ader\u00ada\u00adtive AI and LLM fine-tun\u00ading<\/a>.<\/p>\n\n\n\n

The val\u00adue propo\u00adsi\u00adtion is sim\u00adple: con\u00adsent\u00aded, diverse, qual\u00adi\u00adty-con\u00adtrolled robot\u00adics train\u00ading data, invoiced only for accept\u00aded hours, so your phys\u00adi\u00adcal AI mod\u00adels learn from exam\u00adples that hold up out\u00adside the lab. You can see how this plays out in our case stud\u00adies<\/a> and read more about the stan\u00addards behind it on our about page<\/a>.<\/p>\n\n\n\n

Frequently asked questions<\/strong><\/h2>\n\n\n\n

What is phys\u00adi\u00adcal AI in sim\u00adple terms?<\/strong><\/p>\n\n\n\n

Phys\u00adi\u00adcal AI is arti\u00adfi\u00adcial intel\u00adli\u00adgence that acts in the real world through a body, such as a robot, vehi\u00adcle, or machine, using sen\u00adsors to per\u00adceive and actu\u00ada\u00adtors to move. Unlike a chat\u00adbot, phys\u00adi\u00adcal AI has to obey real physics, so it learns from robot\u00adics train\u00ading data made of real, sim\u00adu\u00adlat\u00aded, and human demon\u00adstra\u00adtion exam\u00adples.<\/p>\n\n\n\n

What is robot\u00adics train\u00ading data?<\/strong><\/p>\n\n\n\n

Robot\u00adics train\u00ading data is the demon\u00adstra\u00adtions, sen\u00adsor record\u00adings, and sim\u00adu\u00adlat\u00aded or gen\u00ader\u00adat\u00aded sce\u00adnar\u00adios that teach a robot how to per\u00adceive and act. It spans human tele\u00adop\u00ader\u00ada\u00adtion, ego\u00adcen\u00adtric first-per\u00adson video, physics sim\u00adu\u00adla\u00adtion, world mod\u00adel out\u00adputs, and pooled cross-embod\u00adi\u00adment datasets.<\/p>\n\n\n\n

Who coined the term phys\u00adi\u00adcal AI?<\/strong><\/p>\n\n\n\n

Nvidia CEO Jensen Huang is wide\u00adly cred\u00adit\u00aded with pop\u00adu\u00adlar\u00adiz\u00ading phys\u00adi\u00adcal AI, and he framed 2026 as the start of a Chat\u00adG\u00adPT moment for the field around CES 2026. The under\u00adly\u00ading research on embod\u00adied AI and robot learn\u00ading is old\u00ader and spans many labs.<\/p>\n\n\n\n

Phys\u00adi\u00adcal AI vs gen\u00ader\u00ada\u00adtive AI: what is the dif\u00adfer\u00adence?<\/strong><\/p>\n\n\n\n

Gen\u00ader\u00ada\u00adtive AI cre\u00adates dig\u00adi\u00adtal con\u00adtent such as text and images, while phys\u00adi\u00adcal AI sens\u00ades and acts in the real world. They are com\u00adple\u00admen\u00adtary: foun\u00adda\u00adtion mod\u00adels give robots rea\u00adson\u00ading and lan\u00adguage skills, and robot\u00adics train\u00ading data teach\u00ades them to turn that rea\u00adson\u00ading into safe phys\u00adi\u00adcal action.<\/p>\n\n\n\n

Is sim\u00adu\u00adla\u00adtion enough to train a robot?<\/strong><\/p>\n\n\n\n

Rarely on its own. Sim\u00adu\u00adla\u00adtion is unbeat\u00adable for scale and safe\u00adty, espe\u00adcial\u00adly for loco\u00admo\u00adtion and nav\u00adi\u00adga\u00adtion, but the real\u00adi\u00adty gap means sim\u00adu\u00adlat\u00aded poli\u00adcies usu\u00adal\u00adly need real demon\u00adstra\u00adtion and ego\u00adcen\u00adtric data to work on hard\u00adware. Most pro\u00adduc\u00adtion sys\u00adtems blend the two.<\/p>\n\n\n\n

What are world mod\u00adels in AI?<\/strong><\/p>\n\n\n\n

World mod\u00adels AI are sys\u00adtems that learn the physics and dynam\u00adics of the real world from data and can then gen\u00ader\u00adate real\u00adis\u00adtic, phys\u00adi\u00adcal\u00adly con\u00adsis\u00adtent sce\u00adnar\u00adios. In robot\u00adics they cre\u00adate syn\u00adthet\u00adic train\u00ading data and eval\u00adu\u00adate poli\u00adcies safe\u00adly before deploy\u00adment.<\/p>\n\n\n\n

How much data does a robot need?<\/strong><\/p>\n\n\n\n

There is no fixed num\u00adber, because it depends on task dex\u00adter\u00adi\u00adty and envi\u00adron\u00adment diver\u00adsi\u00adty. A fixed-loop nav\u00adi\u00adga\u00adtion robot needs far less real data than a gen\u00ader\u00adal-pur\u00adpose manip\u00adu\u00adla\u00adtor. The prac\u00adti\u00adcal answer is to pilot small, inspect fail\u00adures, and add tar\u00adget\u00aded, diverse data until the pol\u00adi\u00adcy gen\u00ader\u00adal\u00adizes.<\/p>\n\n\n\n

Why is ego\u00adcen\u00adtric video impor\u00adtant for embod\u00adied AI?<\/strong><\/p>\n\n\n\n

Ego\u00adcen\u00adtric video cap\u00adtures tasks from a first-per\u00adson view that close\u00adly match\u00ades how a robot per\u00adceives the world, includ\u00ading hand-object inter\u00adac\u00adtion and nat\u00adur\u00adal task sequences. Datasets like Ego4D and Ego-Exo4D made it a cor\u00adner\u00adstone of embod\u00adied AI, and con\u00adsent\u00aded ego\u00adcen\u00adtric cap\u00adture is one of the most scal\u00adable ways to teach robots real skills.<\/p>\n\n\n\n

About the authors<\/strong><\/h2>\n\n\n\n

This guide was writ\u00adten by the Graveiens AI Research Team and reviewed by the Graveiens AI Data Qual\u00adi\u00adty Coun\u00adcil, a group with more than a decade of com\u00adbined expe\u00adri\u00adence in applied machine learn\u00ading data oper\u00ada\u00adtions across com\u00adput\u00ader vision, autonomous sys\u00adtems, and gen\u00ader\u00ada\u00adtive AI. Graveiens AI is a human-in-the-loop data ser\u00advices com\u00adpa\u00adny and is ISO 9001:2017 cer\u00adti\u00adfied, with prac\u00adtices built around explic\u00adit con\u00adsent, per-file audit trails, and mul\u00adti-stage qual\u00adi\u00adty assur\u00adance. Learn more about our team and stan\u00addards on our about page<\/a>, or see why teams choose us<\/a>.<\/p>\n\n\n\n

Conclusion<\/strong><\/h2>\n\n\n\n

The cen\u00adter of grav\u00adi\u00adty in robot\u00adics has moved. Mod\u00adels are increas\u00ading\u00adly open and shared, so the durable advan\u00adtage in phys\u00adi\u00adcal AI now comes from the robot\u00adics train\u00ading data behind the mod\u00adel: how real it is, how diverse it is, and how clean\u00adly it was col\u00adlect\u00aded and con\u00adsent\u00aded. The teams that win will treat data as a sup\u00adply chain to be engi\u00adneered, blend\u00ading sim\u00adu\u00adla\u00adtion, world mod\u00adels, tele\u00adop\u00ader\u00ada\u00adtion, and ego\u00adcen\u00adtric real-world cap\u00adture rather than bet\u00adting every\u00adthing on one tech\u00adnique.<\/p>\n\n\n\n

If you are plan\u00adning a phys\u00adi\u00adcal AI or embod\u00adied AI pro\u00adgram and want con\u00adsent\u00aded, diverse, qual\u00adi\u00adty-con\u00adtrolled robot\u00adics train\u00ading data that holds up out\u00adside the lab, talk to the Graveiens AI team. Book a pilot or con\u00adtact us<\/a> to scope your data mix using the Data Readi\u00adness Matrix above.<\/p>\n\n\n\n

Sources<\/strong><\/h2>\n\n\n\n

\u2022  IBM, What is Phys\u00adi\u00adcal AI?, https:\/\/www.ibm.com\/think\/topics\/physical-ai<\/a><\/p>\n\n\n\n

\u2022  Nvidia News\u00adroom, NVIDIA Releas\u00ades New Phys\u00adi\u00adcal AI Mod\u00adels (CES 2026), https:\/\/nvidianews.nvidia.com\/news\/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots<\/a><\/p>\n\n\n\n

\u2022  Axios, Nvidia CES 2026: Jensen Huang on the Chat\u00adG\u00adPT moment for phys\u00adi\u00adcal AI, https:\/\/www.axios.com\/2026\/01\/05\/nvidia-ces-2026-jensen-huang-speech-ai<\/a><\/p>\n\n\n\n

\u2022  Meta AI, Intro\u00adduc\u00ading Ego-Exo4D, https:\/\/ai.meta.com\/blog\/ego-exo4d-video-learning-perception\/<\/a><\/p>\n\n\n\n

\u2022  Ego4D project site, https:\/\/ego4d-data.org\/<\/a><\/p>\n\n\n\n

\u2022  Open X\u2011Embodiment: Robot\u00adic Learn\u00ading Datasets and RT\u2011X Mod\u00adels, https:\/\/robotics-transformer\u2011x.github.io\/<\/a><\/p>\n\n\n\n

\u2022  DROID: A Large-Scale In-The-Wild Robot Manip\u00adu\u00adla\u00adtion Dataset, https:\/\/droid-dataset.github.io\/<\/a><\/p>\n\n\n\n

\u2022  Phys\u00adi\u00adcal Intel\u00adli\u00adgence, pi0: A Vision-Lan\u00adguage-Action Flow Mod\u00adel, https:\/\/arxiv.org\/abs\/2410.24164<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Phys\u00adi\u00adcal AI is arti\u00adfi\u00adcial intel\u00adli\u00adgence that per\u00adceives, rea\u00adsons, and acts in the real world through robots and machines, and it learns almost entire\u00adly from robot\u00adics train\u00ading data: the\u2026<\/p>\n","protected":false},"author":1,"featured_media":134,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-133","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/133","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/comments?post=133"}],"version-history":[{"count":1,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/133\/revisions"}],"predecessor-version":[{"id":135,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/133\/revisions\/135"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/media\/134"}],"wp:attachment":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/media?parent=133"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/categories?post=133"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/tags?post=133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}