{"id":227,"date":"2026-10-19T18:30:00","date_gmt":"2026-10-19T13:00:00","guid":{"rendered":"https:\/\/www.graveiensai.com\/blog\/?p=227"},"modified":"2026-10-19T18:30:00","modified_gmt":"2026-10-19T13:00:00","slug":"humanoid-robot-training-data","status":"publish","type":"post","link":"https:\/\/www.graveiensai.com\/blog\/humanoid-robot-training-data\/","title":{"rendered":"Humanoid Robot Training Data: What Humanoids Learn From and How It\u2019s Collected"},"content":{"rendered":"\n<p class=\"last-updated wp-block-paragraph\"><em>Last updat\u00aded: Octo\u00adber 2026<\/em><\/p>\n\n\n\n<div class=\"wp-block-group key-takeaways\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><strong>Key take\u00adaways<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Humanoid robot train\u00ading data is time-synced, mul\u00adti\u00admodal data: video, depth, joint states, actions, and lan\u00adguage. It teach\u00ades per\u00adcep\u00adtion, task mean\u00ading, and motor con\u00adtrol togeth\u00ader.<\/li>\n\n\n\n<li>No sin\u00adgle source cov\u00aders every\u00adthing. Ego\u00adcen\u00adtric video brings breadth, tele\u00adop\u00ader\u00ada\u00adtion brings robot-native actions, sim\u00adu\u00adla\u00adtion brings scale, and motion cap\u00adture brings whole-body move\u00adment.<\/li>\n\n\n\n<li>Pub\u00adlished recipes such as NVIDI\u00adA\u2019s GR00T N1 arrange data as a pyra\u00admid. Web and human video form the base, syn\u00adthet\u00adic data the mid\u00addle, and scarce real-robot data the top.<\/li>\n\n\n\n<li>Most dataset prob\u00adlems come from gaps in sync, cal\u00adi\u00adbra\u00adtion, and con\u00adsent, not from too lit\u00adtle vol\u00adume. Write those specs before any\u00adone records.<\/li>\n\n\n\n<li>Start with a small pilot tied to one skill, with clear accep\u00adtance cri\u00adte\u00adria and pay\u00adment only for files that pass review.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">For most robot\u00adics teams, humanoid robot train\u00ading data becomes the bot\u00adtle\u00adneck right after the first demo. A mod\u00adel can bor\u00adrow vision and lan\u00adguage skills from the web. It can\u00adnot bor\u00adrow the motion of open\u00ading a fridge door with a hand that has your robot\u00ad\u2019s joints, cam\u00aderas, and reach.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This guide is for teams that need to build or buy that data. It cov\u00aders what humanoids actu\u00adal\u00adly learn from and how each source is col\u00adlect\u00aded. It also shows how the mix shifts by train\u00ading stage and which sync and con\u00adsent specs decide whether a file is usable. It ends with a prac\u00adti\u00adcal way to scope your first data pro\u00adgram.<\/p>\n\n\n\n<p class=\"cta-inline wp-block-paragraph\"><strong>Plan\u00adning a cap\u00adture pro\u00adgram?<\/strong> <a href=\"https:\/\/www.graveiensai.com\/contact-us\">Scope a pilot billed only on approved files<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What is humanoid robot training data?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Humanoid robot train\u00ading data is a set of time-aligned record\u00adings that pair what a robot, or a human stand-in, sees and sens\u00ades with what it does next. A use\u00adful episode com\u00adbines cam\u00adera video, often depth, body and joint states, actions, and a lan\u00adguage instruc\u00adtion. That lets a mod\u00adel learn per\u00adcep\u00adtion, task intent, and motor con\u00adtrol togeth\u00ader.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>In short:<\/strong> it is demon\u00adstra\u00adtion data with a clock, a body, and a goal attached. Like most robot learn\u00ading from demon\u00adstra\u00adtion data, each step pairs an obser\u00adva\u00adtion with the action a demon\u00adstra\u00adtor took.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It helps to split what a humanoid must learn into four lay\u00aders. Each data source cov\u00aders some lay\u00aders well and oth\u00aders poor\u00adly.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Per\u00adcep\u00adtion.<\/strong> Objects, hands, sur\u00adfaces, and peo\u00adple, seen from many angles and in many light\u00ading con\u00addi\u00adtions.<\/li>\n\n\n\n<li><strong>Task seman\u00adtics.<\/strong> What \u201cput the cup away\u201d means, which steps it involves, and when each step is done.<\/li>\n\n\n\n<li><strong>Actions and pro\u00adpri\u00ado\u00adcep\u00adtion.<\/strong> Joint posi\u00adtions, hand or grip\u00adper states, end-effec\u00adtor pos\u00ades, and some\u00adtimes forces, tied to each frame.<\/li>\n\n\n\n<li><strong>Whole-body con\u00adtrol.<\/strong> Bal\u00adance, step\u00adping, reach\u00ading high and low, and car\u00adry\u00ading objects while mov\u00ading.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Web video is rich in the first two lay\u00aders and thin on the last two. That is why phys\u00adi\u00adcal AI data pro\u00adgrams look so dif\u00adfer\u00adent from image-label\u00ading projects. For the wider con\u00adtext, see our <a href=\"https:\/\/www.graveiensai.com\/blog\/physical-ai-robotics-training-data\/\">guide to phys\u00adi\u00adcal AI and robot\u00adics train\u00ading data<\/a>. If you are new\u00ader to the mod\u00adel\u00ading side, our explain\u00ader on <a href=\"https:\/\/www.graveiensai.com\/blog\/how-do-robots-learn\/\">how robots learn<\/a> cov\u00aders the basics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The embodiment gap<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Every source except tele\u00adop\u00ader\u00ada\u00adtion on your own robot car\u00adries an embod\u00adi\u00adment gap. A human head cam\u00adera sits at a dif\u00adfer\u00adent height and angle from a robot\u00ad\u2019s cam\u00aderas. Human hands, arm lengths, and joint lim\u00adits also dif\u00adfer from the robot\u00ad\u2019s.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">So data record\u00aded on one body has to be trans\u00adlat\u00aded to anoth\u00ader. The Ego\u00adHu\u00admanoid team, for exam\u00adple, built two align\u00adment steps for this. One is view align\u00adment, which reduces dif\u00adfer\u00adences caused by cam\u00adera height and per\u00adspec\u00adtive. The oth\u00ader is action align\u00adment, which maps human motion into a kine\u00admat\u00adi\u00adcal\u00adly fea\u00adsi\u00adble action space for the humanoid (<a href=\"https:\/\/arxiv.org\/abs\/2602.10106\" rel=\"noopener\" target=\"_blank\">Shi et al., arX\u00adiv 2602.10106<\/a>).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Where does humanoid robot training data come from? Four sources compared<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Humanoid robot train\u00ading data comes from four main sources: ego\u00adcen\u00adtric video of peo\u00adple doing tasks, tele\u00adop\u00ader\u00ada\u00adtion of the tar\u00adget robot, sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic gen\u00ader\u00ada\u00adtion, and motion cap\u00adture of human bod\u00adies. Each teach\u00ades a dif\u00adfer\u00adent lay\u00ader. Real pro\u00adgrams blend them, using cheap, broad sources for gen\u00ader\u00adal pri\u00adors and robot-native data for final con\u00adtrol.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"840\" src=\"https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-sources-comparison-table.webp\" alt=\"Comparison table of four humanoid data sources: egocentric video, teleoperation, simulation and motion capture, showing what each teaches, its action labels and its main limit\" class=\"wp-image-224\" srcset=\"https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-sources-comparison-table.webp 1600w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-sources-comparison-table-300x158.webp 300w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-sources-comparison-table-1024x538.webp 1024w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-sources-comparison-table-768x403.webp 768w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-sources-comparison-table-1536x806.webp 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><figcaption class=\"wp-element-caption\">What each data source teach\u00ades, whether it car\u00adries action labels, and where it falls short.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Source<\/th><th>What it teach\u00ades best<\/th><th>Action labels<\/th><th>Main lim\u00adit<\/th><\/tr><\/thead><tbody><tr><td>Ego\u00adcen\u00adtric human video<\/td><td>Per\u00adcep\u00adtion, hand-object inter\u00adac\u00adtion, and task steps across many real places<\/td><td>None direct\u00adly; inferred, retar\u00adget\u00aded, or learned as latent actions<\/td><td>Human view\u00adpoint and hands dif\u00adfer from the robot\u00ad\u2019s<\/td><\/tr><tr><td>Tele\u00adop\u00ader\u00ada\u00adtion on the robot<\/td><td>Robot-native actions, tim\u00ading, con\u00adtact, and recov\u00adery<\/td><td>Yes; joint states and com\u00admands logged on the robot<\/td><td>Slow and hard\u00adware-bound; each hour needs a robot, a rig, and a trained oper\u00ada\u00adtor<\/td><\/tr><tr><td>Sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic data<\/td><td>Scale, vari\u00ada\u00adtion, and rare or unsafe cas\u00ades<\/td><td>Yes in physics sim\u00adu\u00adla\u00adtion; inferred for gen\u00ader\u00adat\u00aded video<\/td><td>Sim-to-real gap in con\u00adtact, mate\u00adri\u00adals, and sen\u00adsor noise<\/td><\/tr><tr><td>Motion cap\u00adture<\/td><td>Whole-body kine\u00admat\u00adics, bal\u00adance, and nat\u00adur\u00adal pos\u00adture<\/td><td>Human body pos\u00ades that need retar\u00adget\u00ading to robot joints<\/td><td>Usu\u00adal\u00adly stu\u00addio-bound; no robot cam\u00adera view unless paired with one<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Egocentric video for robotics<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Ego\u00adcen\u00adtric video is first-per\u00adson footage of peo\u00adple doing real tasks, record\u00aded from a head-mount\u00aded or chest-mount\u00aded cam\u00adera. It is the cheap\u00adest way to cov\u00ader many homes, kitchens, shops, and work sites. It also cap\u00adtures hands and objects from a view\u00adpoint close to a humanoid\u2019s head cam\u00adera.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Scale and vari\u00adety are its strengths. As of Octo\u00adber 2026, the Ego4D project site lists 3,670 hours of video from 923 par\u00adtic\u00adi\u00adpants in 74 loca\u00adtions across 9 coun\u00adtries. The team delib\u00ader\u00adate\u00adly used sev\u00aden dif\u00adfer\u00adent head-mount\u00aded cam\u00adera mod\u00adels to avoid over\u00adfit\u00adting to a sin\u00adgle device (<a href=\"https:\/\/ego4d-data.org\/\" rel=\"noopener\" target=\"_blank\">Ego4D<\/a>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The catch is actions. Human video has no joint com\u00admands. The GR00T N1 paper notes that its human video datasets lack explic\u00adit action labels. They still cap\u00adture \u201caffor\u00addances, task seman\u00adtics, and nat\u00adur\u00adal motion pat\u00adterns.\u201d To use that footage for con\u00adtrol, the authors trained a latent-action mod\u00adel and an inverse dynam\u00adics mod\u00adel to add pseu\u00addo-actions (<a href=\"https:\/\/arxiv.org\/abs\/2503.14734\" rel=\"noopener\" target=\"_blank\">NVIDIA, arX\u00adiv 2503.14734<\/a>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Cus\u00adtom col\u00adlec\u00adtion clos\u00ades gaps that pub\u00adlic sets leave open. Exam\u00adples include your tar\u00adget tasks, your envi\u00adron\u00adments, and a cam\u00adera rig matched to your robot\u00ad\u2019s field of view. That is the work our <a href=\"https:\/\/www.graveiensai.com\/egocentric-video-data-collection\">ego\u00adcen\u00adtric video data col\u00adlec\u00adtion ser\u00advice<\/a> is built around.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Teleoperation data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In tele\u00adop\u00ader\u00ada\u00adtion, a trained oper\u00ada\u00adtor dri\u00adves the real robot, or a matched rig, through a task in real time. Con\u00adtrol comes through leader-fol\u00adlow\u00ader arms, a VR head\u00adset with con\u00adtrollers, or sim\u00adi\u00adlar devices. Because the robot logs its own joint states and com\u00admands, the action labels are native. Noth\u00ading has to be esti\u00admat\u00aded.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The cost is through\u00adput. DROID, a large open dataset for a sin\u00adgle robot arm rather than a humanoid, shows the effort involved. Its team col\u00adlect\u00aded 76,000 demon\u00adstra\u00adtion tra\u00adjec\u00adto\u00adries, or 350 hours, across 564 scenes and 86 tasks. The work took 50 data col\u00adlec\u00adtors on three con\u00adti\u00adnents about 12 months. Each sta\u00adtion paired a 7\u2011DoF arm with two adjustable stereo cam\u00aderas, a wrist stereo cam\u00adera, and a VR head\u00adset for tele\u00adop\u00ader\u00ada\u00adtion (<a href=\"https:\/\/droid-dataset.github.io\/\" rel=\"noopener\" target=\"_blank\">DROID<\/a>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For humanoids, tele\u00adop\u00ader\u00ada\u00adtion data usu\u00adal\u00adly sits at the top of the stack, where it anchors the pol\u00adi\u00adcy to the actu\u00adal hard\u00adware. See our <a href=\"https:\/\/www.graveiensai.com\/teleoperation-data-services\">tele\u00adop\u00ader\u00ada\u00adtion data ser\u00advices<\/a> for how oper\u00ada\u00adtor ses\u00adsions are run.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Simulation and synthetic data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Sim\u00adu\u00adla\u00adtion pro\u00adduces labeled tra\u00adjec\u00adto\u00adries at a scale real cap\u00adture can\u00adnot match. It also han\u00addles cas\u00ades you can\u00adnot safe\u00adly stage, like a dropped glass near a per\u00adson. The GR00T N1 team start\u00aded from a few dozen tele\u00adop\u00ader\u00adat\u00aded source demon\u00adstra\u00adtions. From those, a sim\u00adu\u00adla\u00adtion tool called DexMim\u00adic\u00adGen gen\u00ader\u00adat\u00aded 540,000 new demon\u00adstra\u00adtions for 54 source-and-tar\u00adget com\u00adbi\u00adna\u00adtions in table\u00adtop tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same paper used video-gen\u00ader\u00ada\u00adtion mod\u00adels to cre\u00adate \u201cneur\u00adal tra\u00adjec\u00adto\u00adries.\u201d This grew its in-house tele\u00adop\u00ader\u00ada\u00adtion data from 88 hours to rough\u00adly 827 hours. The pat\u00adtern is con\u00adsis\u00adtent: syn\u00adthet\u00adic data mul\u00adti\u00adplies a real seed. It does not replace it, because con\u00adtact physics, mate\u00adri\u00adals, and sen\u00adsor noise still dif\u00adfer from the real world. Our <a href=\"https:\/\/www.graveiensai.com\/simulation-synthetic-data\">sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic data<\/a> work fol\u00adlows the same prin\u00adci\u00adple.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Motion capture and human-to-humanoid retargeting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Motion cap\u00adture records full-body pose using opti\u00adcal mark\u00aders, iner\u00adtial suits, or VR track\u00aders. It is the best source for bal\u00adance, step\u00adping, crouch\u00ading, and nat\u00adur\u00adal pos\u00adture. Those are exact\u00adly the whole-body con\u00adtrol skills that hand-focused video miss\u00ades.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Mocap data must be retar\u00adget\u00aded to the robot\u00ad\u2019s skele\u00adton, which means respect\u00ading joint lim\u00adits, foot con\u00adtacts, and mass dis\u00adtri\u00adb\u00adu\u00adtion. It also lacks a robot-view cam\u00adera, so pair it with a head or chest cam\u00adera if you are train\u00ading a vision-dri\u00adven pol\u00adi\u00adcy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How should the data mix change by task stage?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The mix should move from broad to robot-spe\u00adcif\u00adic as train\u00ading pro\u00adgress\u00ades. Ear\u00adly pre\u00adtrain\u00ading favors large vol\u00adumes of human and web video for gen\u00ader\u00adal pri\u00adors. Mid\u00addle stages add sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic data for scale. Final post-train\u00ading leans on tele\u00adop\u00ader\u00ada\u00adtion from the tar\u00adget robot, plus eval\u00adu\u00ada\u00adtion sets record\u00aded in real deploy\u00adment set\u00adtings.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The GR00T N1 paper describes this as a data pyra\u00admid. Web data and human videos form the base. Syn\u00adthet\u00adic data from physics sim\u00adu\u00adla\u00adtion or neur\u00adal gen\u00ader\u00ada\u00adtion forms the mid\u00addle. Real-robot data com\u00adpletes the top. In the authors\u2019 words, \u201cdata quan\u00adti\u00adty decreas\u00ades, and embod\u00adi\u00adment-speci\u00adfici\u00adty increas\u00ades, mov\u00ading from the bot\u00adtom to the top.\u201d<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Stage<\/th><th>Goal<\/th><th>Main sources<\/th><th>What to check<\/th><\/tr><\/thead><tbody><tr><td>Pre\u00adtrain\u00ading<\/td><td>Visu\u00adal and behav\u00adioral pri\u00adors<\/td><td>Web video, pub\u00adlic and cus\u00adtom ego\u00adcen\u00adtric video<\/td><td>Vari\u00adety of places, objects, and hands; license terms<\/td><\/tr><tr><td>Skill expan\u00adsion<\/td><td>More task vari\u00adants and rare cas\u00ades<\/td><td>Sim\u00adu\u00adla\u00adtion, syn\u00adthet\u00adic aug\u00admen\u00adta\u00adtion, motion cap\u00adture<\/td><td>Ran\u00addom\u00adized scenes; sim assets that match real objects<\/td><\/tr><tr><td>Embod\u00adi\u00adment post-train\u00ading<\/td><td>Robot-native con\u00adtrol for tar\u00adget skills<\/td><td>Tele\u00adop\u00ader\u00ada\u00adtion on the tar\u00adget robot or a matched rig<\/td><td>Sync, cal\u00adi\u00adbra\u00adtion, suc\u00adcess labels, recov\u00adery demos<\/td><\/tr><tr><td>Eval\u00adu\u00ada\u00adtion<\/td><td>Mea\u00adsure real per\u00adfor\u00admance<\/td><td>Held-out real episodes in deploy\u00adment-like set\u00adtings<\/td><td>Nev\u00ader mixed into train\u00ading; cov\u00aders edge cas\u00ades<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">There is no uni\u00adver\u00adsal ratio. Avoid copy\u00ading a mix from a paper. The right weight\u00ading depends on your mod\u00adel, your robot, and how far your tar\u00adget tasks sit from pub\u00adlic data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The GRASP test: five questions for any humanoid dataset<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before you pay for a batch of data, whether pub\u00adlic, syn\u00adthet\u00adic, or cus\u00adtom, run it through five ques\u00adtions. We call this the GRASP test. It turns a vague \u201cis this use\u00adful?\u201d into a score\u00adcard your ML lead, ops lead, and legal review\u00ader can all read.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"880\" src=\"https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/grasp-humanoid-dataset-test-framework.webp\" alt=\"The GRASP test framework with five rows: Goal, Robot match, Action signal, Sync and calibration, and Permissions, each with a guiding question\" class=\"wp-image-225\" srcset=\"https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/grasp-humanoid-dataset-test-framework.webp 1600w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/grasp-humanoid-dataset-test-framework-300x165.webp 300w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/grasp-humanoid-dataset-test-framework-1024x563.webp 1024w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/grasp-humanoid-dataset-test-framework-768x422.webp 768w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/grasp-humanoid-dataset-test-framework-1536x845.webp 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><figcaption class=\"wp-element-caption\">The GRASP test: score each can\u00addi\u00addate dataset on goal, robot match, action sig\u00adnal, sync, and per\u00admis\u00adsions.<\/figcaption><\/figure>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>G: Goal.<\/strong> Which skill does this batch serve, and how will you mea\u00adsure suc\u00adcess? A batch with\u00adout a tar\u00adget skill is just stor\u00adage cost.<\/li>\n\n\n\n<li><strong>R: Robot match.<\/strong> How close are the view\u00adpoint, body pro\u00adpor\u00adtions, and hands to your robot? Check cam\u00adera height, field of view, and hand type.<\/li>\n\n\n\n<li><strong>A: Action sig\u00adnal.<\/strong> Does the data car\u00adry robot actions, human pos\u00ades that need retar\u00adget\u00ading, or no actions at all? Know this before you bud\u00adget for label\u00ading.<\/li>\n\n\n\n<li><strong>S: Sync and cal\u00adi\u00adbra\u00adtion.<\/strong> Are all streams on one clock? Are cam\u00adera intrin\u00adsics and extrin\u00adsics logged for every ses\u00adsion?<\/li>\n\n\n\n<li><strong>P: Per\u00admis\u00adsions.<\/strong> Do you hold con\u00adsent, a license that cov\u00aders com\u00admer\u00adcial train\u00ading, and the abil\u00adi\u00adty to hon\u00ador dele\u00adtion requests?<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>How to score it.<\/strong> Rate each let\u00adter 0, 1, or 2. A zero on S or P is a stop, because no mod\u00adel fix repairs bad tim\u00ading or miss\u00ading con\u00adsent. A low R score is fine for pre\u00adtrain\u00ading but not for post-train\u00ading. A low A score means you need a plan, such as retar\u00adget\u00ading or latent actions, before the data earns its keep.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pub\u00adlic ego\u00adcen\u00adtric video, for instance, can score well on G for per\u00adcep\u00adtion pre\u00adtrain\u00ading. It usu\u00adal\u00adly scores low on R and A. Its P score depends on the license; Ego4D, for exam\u00adple, requires you to sign a license agree\u00adment before down\u00adload. Tele\u00adop\u00ader\u00ada\u00adtion on your own robot scores high on R and A, but cost lim\u00adits its vol\u00adume.<\/p>\n\n\n\n<p class=\"cta-inline wp-block-paragraph\"><strong>Turn\u00ading GRASP into a cap\u00adture spec?<\/strong> <a href=\"https:\/\/www.graveiensai.com\/contact-us\">Ask us for the CAPTURES check\u00adlist<\/a>, a field-by-field list of the sync, meta\u00adda\u00adta, and con\u00adsent items to agree before a pilot.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What quality and sync specs should a humanoid dataset meet?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A humanoid dataset should put every stream on one clock and log cam\u00adera cal\u00adi\u00adbra\u00adtion per ses\u00adsion. It should record joint states and actions at the robot\u00ad\u2019s con\u00adtrol rate and seg\u00adment episodes with lan\u00adguage and suc\u00adcess labels. Write these as numer\u00adic accep\u00adtance cri\u00adte\u00adria before cap\u00adture, so review\u00aders approve or reject each file against the same rules.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>In short:<\/strong> if it is not in the spec, it will not be in the data.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>One clock.<\/strong> Use hard\u00adware sync or a shared time source across cam\u00aderas, IMUs, and robot logs. Keep per-frame time\u00adstamps, not per-file ones, and set a max\u00adi\u00admum allowed off\u00adset in the spec.<\/li>\n\n\n\n<li><strong>Cal\u00adi\u00adbra\u00adtion.<\/strong> Log intrin\u00adsics and extrin\u00adsics for every cam\u00adera, and re-check them when\u00adev\u00ader a rig is moved or bumped. DROID cov\u00aders 1,417 cam\u00adera view\u00adpoints with stereo cal\u00adi\u00adbra\u00adtion. Even so, in April 2025 its team released improved cal\u00adi\u00adbra\u00adtions for 36,000 episodes. Treat cal\u00adi\u00adbra\u00adtion as part of the deliv\u00ader\u00adable, because fix\u00ading it lat\u00ader is expen\u00adsive.<\/li>\n\n\n\n<li><strong>Rates.<\/strong> State frame rate, res\u00ado\u00adlu\u00adtion, and con\u00adtrol rate up front. GR00T N1\u2019s action mod\u00adule gen\u00ader\u00adates motor actions at 120 Hz. At rates like that, a few frames of drift between video and joint states turns into label noise.<\/li>\n\n\n\n<li><strong>Episode struc\u00adture.<\/strong> Each episode needs a clear start and end, a lan\u00adguage instruc\u00adtion, and a suc\u00adcess or fail\u00adure flag. Keep recov\u00adery attempts instead of trim\u00adming them, because they teach the pol\u00adi\u00adcy how to fix mis\u00adtakes.<\/li>\n\n\n\n<li><strong>Meta\u00adda\u00adta.<\/strong> Record rig ID, envi\u00adron\u00adment type, light\u00ading, objects, a pseu\u00addo\u00adny\u00admous par\u00adtic\u00adi\u00adpant ID, and a con\u00adsent ref\u00ader\u00adence.<\/li>\n\n\n\n<li><strong>Cov\u00ader\u00adage plan.<\/strong> Plan vari\u00ada\u00adtion across rooms, objects, light\u00ading, oper\u00ada\u00adtors, and devices. Ego4D\u2019s choice of sev\u00aden cam\u00adera mod\u00adels to avoid sin\u00adgle-device over\u00adfit\u00adting is a good mod\u00adel.<\/li>\n\n\n\n<li><strong>For\u00admat.<\/strong> Match your train\u00ading loader. DROID, for exam\u00adple, ships episodes that pair exte\u00adri\u00ador and wrist cam\u00adera images with an action and a lan\u00adguage instruc\u00adtion at each step.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Common quality failures<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Dropped frames hid\u00adden by re-encod\u00ading, which breaks frame-to-action align\u00adment.<\/li>\n\n\n\n<li>Slow clock drift across long ses\u00adsions that nobody mea\u00adsured.<\/li>\n\n\n\n<li>Hands leav\u00ading the frame dur\u00ading the crit\u00adi\u00adcal grasp.<\/li>\n\n\n\n<li>Oper\u00ada\u00adtors mov\u00ading unnat\u00adu\u00adral\u00adly fast or slow to \u201cfin\u00adish the episode.\u201d<\/li>\n\n\n\n<li>Fail\u00adures trimmed out, leav\u00ading the pol\u00adi\u00adcy with no exam\u00adples of recov\u00adery.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Consent and privacy in humanoid robot data collection<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Humanoid robot data col\u00adlec\u00adtion often hap\u00adpens in homes, shops, and work sites. That means it records faces, voic\u00ades, screens, doc\u00adu\u00adments, and bystanders, not just the task. Con\u00adsent has to cov\u00ader the par\u00adtic\u00adi\u00adpant, the site, and any\u00adone else like\u00adly to appear. Every file should be trace\u00adable back to that con\u00adsent.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Par\u00adtic\u00adi\u00adpant con\u00adsent.<\/strong> Get explic\u00adit, informed con\u00adsent that names the pur\u00adpose (robot mod\u00adel train\u00ading), the data types cap\u00adtured, reten\u00adtion, and how to with\u00addraw.<\/li>\n\n\n\n<li><strong>Site per\u00admis\u00adsion.<\/strong> Get writ\u00adten per\u00admis\u00adsion from the own\u00ader or oper\u00ada\u00adtor of each loca\u00adtion.<\/li>\n\n\n\n<li><strong>Bystanders.<\/strong> Decide in advance whether to avoid them, blur them, or obtain their con\u00adsent, and brief cap\u00adture teams on the rule.<\/li>\n\n\n\n<li><strong>De-iden\u00adti\u00adfi\u00adca\u00adtion.<\/strong> Blur faces and read\u00adable screens where con\u00adsent does not cov\u00ader them, and review a sam\u00adple before deliv\u00adery.<\/li>\n\n\n\n<li><strong>Oper\u00ada\u00adtors count too.<\/strong> In tele\u00adop\u00ader\u00ada\u00adtion, cam\u00aderas and micro\u00adphones may cap\u00adture the oper\u00ada\u00adtor, so they need con\u00adsent as well.<\/li>\n\n\n\n<li><strong>Pub\u00adlic datasets have terms.<\/strong> Ego4D states that col\u00adlect\u00ading part\u00adners hold con\u00adsent or release forms for all videos and that most videos were de-iden\u00adti\u00adfied before release. Access still requires a signed license agree\u00adment, so check that your use fits it.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Teams col\u00adlect\u00ading in India should map their con\u00adsent flow to the Dig\u00adi\u00adtal Per\u00adson\u00adal Data Pro\u00adtec\u00adtion Act, and teams cap\u00adtur\u00ading in the EU to the GDPR. Our <a href=\"https:\/\/www.graveiensai.com\/blog\/egocentric-data-collection-vendor\/\">guide to choos\u00ading an ego\u00adcen\u00adtric data col\u00adlec\u00adtion ven\u00addor<\/a> shows which con\u00adsent records to ask for before you sign.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How do you scope a humanoid data program?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Scope a humanoid data pro\u00adgram around one or two tar\u00adget skills, not a data vol\u00adume. Map your robot\u00ad\u2019s cam\u00aderas and body, pick sources with the GRASP test, and write a cap\u00adture spec with accep\u00adtance cri\u00adte\u00adria. Then run a small pilot. Scale only after reviewed files show the spec works in your train\u00ading pipeline.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Put sim\u00adply, treat humanoid robot train\u00ading data as a prod\u00aduct with a spec, not a com\u00admod\u00adi\u00adty bought by the hour.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"600\" src=\"https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-program-scoping-process.webp\" alt=\"Five-step process to scope a humanoid data program: pick target skills, map the embodiment, choose the mix, write the spec, and pilot, approve and scale\" class=\"wp-image-226\" srcset=\"https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-program-scoping-process.webp 1600w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-program-scoping-process-300x113.webp 300w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-program-scoping-process-1024x384.webp 1024w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-program-scoping-process-768x288.webp 768w, https:\/\/www.graveiensai.com\/blog\/wp-content\/uploads\/2026\/10\/humanoid-data-program-scoping-process-1536x576.webp 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><figcaption class=\"wp-element-caption\">Scop\u00ading a humanoid data pro\u00adgram in five steps, from tar\u00adget skills to an approved-file pilot.<\/figcaption><\/figure>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Pick tar\u00adget skills.<\/strong> Choose one or two skills, such as \u201cload a dish\u00adwash\u00ader rack,\u201d with a suc\u00adcess def\u00adi\u00adn\u00adi\u00adtion you can mea\u00adsure on the robot.<\/li>\n\n\n\n<li><strong>Map the embod\u00adi\u00adment.<\/strong> List cam\u00adera posi\u00adtions, field of view, hand type, degrees of free\u00addom, and con\u00adtrol rate. This becomes the ref\u00ader\u00adence for rig design.<\/li>\n\n\n\n<li><strong>Choose the mix.<\/strong> Run each can\u00addi\u00addate source through GRASP. Decide what comes from pub\u00adlic data, cus\u00adtom ego\u00adcen\u00adtric cap\u00adture, sim\u00adu\u00adla\u00adtion, and tele\u00adop\u00ader\u00ada\u00adtion.<\/li>\n\n\n\n<li><strong>Write the spec.<\/strong> Set sync tol\u00ader\u00adance, cal\u00adi\u00adbra\u00adtion rou\u00adtine, meta\u00adda\u00adta fields, con\u00adsent forms, and per-file accep\u00adtance rules. Include what counts as a reject\u00aded file.<\/li>\n\n\n\n<li><strong>Pilot, approve, scale.<\/strong> Cap\u00adture a small batch, review every file against the spec, and load approved files into your pipeline. Fix the spec, then expand.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">What drives cost and effort<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Exact bud\u00adgets depend on your scope, but the same dri\u00advers show up in every pro\u00adgram.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Task com\u00adplex\u00adi\u00adty.<\/strong> Biman\u00adu\u00adal, long-hori\u00adzon, or con\u00adtact-heavy tasks take longer per episode and need more oper\u00ada\u00adtor train\u00ading.<\/li>\n\n\n\n<li><strong>Robot time.<\/strong> Tele\u00adop\u00ader\u00ada\u00adtion vol\u00adume is capped by the num\u00adber of robots or matched rigs avail\u00adable.<\/li>\n\n\n\n<li><strong>Envi\u00adron\u00adment access.<\/strong> Homes and indus\u00adtri\u00adal sites take time to recruit, sched\u00adule, and clear for con\u00adsent.<\/li>\n\n\n\n<li><strong>Sen\u00adsor stack.<\/strong> Depth, stereo, IMUs, and gloves add set\u00adup, cal\u00adi\u00adbra\u00adtion, and stor\u00adage.<\/li>\n\n\n\n<li><strong>Anno\u00adta\u00adtion depth.<\/strong> Step seg\u00adments, lan\u00adguage labels, and hand key\u00adpoints each add review pass\u00ades.<\/li>\n\n\n\n<li><strong>Rejec\u00adtion rate.<\/strong> A tight spec rais\u00ades ear\u00adly rejec\u00adtions but low\u00aders rework lat\u00ader.<\/li>\n\n\n\n<li><strong>Pri\u00adva\u00adcy pro\u00adcess\u00ading.<\/strong> Blur\u00adring and con\u00adsent review scale with how pub\u00adlic the cap\u00adture set\u00adting is.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Mistakes to avoid<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Buy\u00ading hours instead of skills. Vol\u00adume with\u00adout a tar\u00adget skill rarely moves a pol\u00adi\u00adcy.<\/li>\n\n\n\n<li>Ignor\u00ading view\u00adpoint. A chest cam\u00adera and a head cam\u00adera teach dif\u00adfer\u00adent things.<\/li>\n\n\n\n<li>Treat\u00ading cal\u00adi\u00adbra\u00adtion as an after\u00adthought.<\/li>\n\n\n\n<li>Throw\u00ading away failed attempts.<\/li>\n\n\n\n<li>Eval\u00adu\u00adat\u00ading in the same rooms you trained in.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">How Graveiens AI supports humanoid data programs<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Graveiens AI runs human data col\u00adlec\u00adtion for phys\u00adi\u00adcal AI teams, with con\u00adsent and file-lev\u00adel review built into the work\u00adflow. Here is what that looks like for humanoid robot train\u00ading data.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Explic\u00adit-con\u00adsent col\u00adlec\u00adtion.<\/strong> Every project fol\u00adlows the same path: par\u00adtic\u00adi\u00adpant onboard\u00ading, then record\u00ading and cap\u00adture man\u00adage\u00adment, then meta\u00adda\u00adta tag\u00adging, then QA.<\/li>\n\n\n\n<li><strong>Rigs matched to your robot.<\/strong> We use head-mount\u00aded smart\u00adphone rigs, action cam\u00aderas, stereo and depth rigs, VR head\u00adsets, AI smart glass\u00ades, and cus\u00adtom rigs matched to a robot\u00ad\u2019s cam\u00adera field of view.<\/li>\n\n\n\n<li><strong>Real envi\u00adron\u00adments.<\/strong> We cap\u00adture at indus\u00adtri\u00adal sites, in agri\u00adcul\u00adtur\u00adal work, in house\u00adholds, on retail floors, and in kitchens.<\/li>\n\n\n\n<li><strong>Tele\u00adop\u00ader\u00ada\u00adtion and sim\u00adu\u00adla\u00adtion.<\/strong> These are new\u00ader offer\u00adings, and we are onboard\u00ading pilot part\u00adners. Tele\u00adop\u00ader\u00ada\u00adtion uses trained oper\u00ada\u00adtors on leader-fol\u00adlow\u00ader and VR rigs. Sim\u00adu\u00adla\u00adtion work builds on open tools so out\u00adputs stay portable.<\/li>\n\n\n\n<li><strong>Lan\u00adguage cov\u00ader\u00adage.<\/strong> We sup\u00adport 25+ lan\u00adguages, includ\u00ading deep Indic cov\u00ader\u00adage, for spo\u00adken task nar\u00adra\u00adtion and instruc\u00adtions.<\/li>\n\n\n\n<li><strong>Billing on approved files.<\/strong> You pay for deliv\u00ader\u00adables that pass the accep\u00adtance cri\u00adte\u00adria agreed at scop\u00ading.<\/li>\n\n\n\n<li><strong>Cer\u00adti\u00adfied qual\u00adi\u00adty sys\u00adtem.<\/strong> Graveiens AI is ISO 9001 cer\u00adti\u00adfied.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Need some\u00adthing out\u00adside that list, such as full-body motion cap\u00adture? We scope this with you.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently asked questions<\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1760100000000\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">What data are humanoid robots trained on?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Humanoid robots are trained on a blend of sources. These include web and human video, ego\u00adcen\u00adtric record\u00adings of peo\u00adple doing tasks, sim\u00adu\u00adla\u00adtion and syn\u00adthet\u00adic tra\u00adjec\u00adto\u00adries, motion cap\u00adture, and tele\u00adop\u00ader\u00ada\u00adtion data record\u00aded on the robot itself. The broad sources teach per\u00adcep\u00adtion and task mean\u00ading. Robot-native data teach\u00ades the exact joint-lev\u00adel actions, tim\u00ading, and con\u00adtact the pol\u00adi\u00adcy needs to con\u00adtrol real hard\u00adware.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1760100000001\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">How is humanoid robot data collected?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Teams col\u00adlect it in four main ways. Peo\u00adple wear head or chest cam\u00aderas while doing real tasks. Trained oper\u00ada\u00adtors tele\u00adop\u00ader\u00adate the robot through demon\u00adstra\u00adtions. Sim\u00adu\u00adla\u00adtors gen\u00ader\u00adate tra\u00adjec\u00adto\u00adries at scale. Motion cap\u00adture sys\u00adtems record full-body move\u00adment. Every stream is time-synced, cal\u00adi\u00adbrat\u00aded, labeled with the task, and reviewed against accep\u00adtance cri\u00adte\u00adria before train\u00ading.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1760100000002\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">Can humanoid robots learn from human videos?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, with extra work. Human video teach\u00ades per\u00adcep\u00adtion, task steps, and how hands inter\u00adact with objects, but it has no robot actions. Researchers infer actions with latent-action or inverse dynam\u00adics mod\u00adels, or retar\u00adget human motion to the robot. They also align view\u00adpoints. Human video then sup\u00adple\u00adments, rather than replaces, a small\u00ader set of robot-native demon\u00adstra\u00adtions.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1760100000003\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">What is teleoperation data in robotics?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Tele\u00adop\u00ader\u00ada\u00adtion data is record\u00aded while a human oper\u00ada\u00adtor con\u00adtrols a robot in real time, using leader-fol\u00adlow\u00ader arms, VR con\u00adtrollers, or sim\u00adi\u00adlar devices. The robot logs its own joint states, com\u00admands, and cam\u00adera feeds. That makes the action labels native to the hard\u00adware, which is why tele\u00adop\u00ader\u00ada\u00adtion usu\u00adal\u00adly anchors the final train\u00ading stage.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1760100000004\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">Is synthetic data enough to train a humanoid robot?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Not on its own. Sim\u00adu\u00adla\u00adtion and gen\u00ader\u00adat\u00aded video add scale, vari\u00ada\u00adtion, and rare cas\u00ades cheap\u00adly. Pub\u00adlished work such as GR00T N1 uses them to mul\u00adti\u00adply a small set of real demon\u00adstra\u00adtions. But con\u00adtact physics, mate\u00adri\u00adals, and sen\u00adsor noise still dif\u00adfer from real\u00adi\u00adty. Real tele\u00adop\u00ader\u00ada\u00adtion and real-world eval\u00adu\u00ada\u00adtion data remain nec\u00ades\u00adsary to close that gap.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1760100000005\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">How much training data does a humanoid robot need?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>There is no fixed num\u00adber. It depends on the mod\u00adel, the robot, and how far the tar\u00adget skills sit from pub\u00adlic data. A bet\u00adter approach is to scope by skill. Define suc\u00adcess for one or two tasks, run a pilot, and mea\u00adsure results on the robot. Then scale the sources that move per\u00adfor\u00admance.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1760100000006\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">Do you need consent to record egocentric video for robot training?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Ego\u00adcen\u00adtric footage cap\u00adtures the wear\u00ader and often bystanders, homes, screens, and voic\u00ades. Get explic\u00adit, informed con\u00adsent from par\u00adtic\u00adi\u00adpants and site own\u00aders. Set rules for bystanders, de-iden\u00adti\u00adfy where con\u00adsent does not reach, and link every file to its con\u00adsent record. In India, align the process with the Dig\u00adi\u00adtal Per\u00adson\u00adal Data Pro\u00adtec\u00adtion Act.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n<div class=\"wp-block-group cta-box\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<h3 class=\"wp-block-heading\">Scope a pilot billed only on approved files<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Tell us your robot, your tar\u00adget skills, and where it will work. We will pro\u00adpose a cap\u00adture mix, a spec with accep\u00adtance cri\u00adte\u00adria, and a small pilot. You pay only for files that pass review.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.graveiensai.com\/contact-us\">Scope a pilot billed only on approved files<\/a>. You can also ask us for the CAPTURES check\u00adlist.<\/p>\n<\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Sources<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>NVIDIA et al., \u201cGR00T N1: An Open Foun\u00adda\u00adtion Mod\u00adel for Gen\u00ader\u00adal\u00adist Humanoid Robots,\u201d arXiv:2503.14734. <a href=\"https:\/\/arxiv.org\/abs\/2503.14734\" rel=\"noopener\" target=\"_blank\">arxiv.org\/abs\/2503.14734<\/a><\/li>\n\n\n\n<li>DROID Dataset Team, \u201cDROID: A Large-Scale In-the-Wild Robot Manip\u00adu\u00adla\u00adtion Dataset,\u201d project page. <a href=\"https:\/\/droid-dataset.github.io\/\" rel=\"noopener\" target=\"_blank\">droid-dataset.github.io<\/a><\/li>\n\n\n\n<li>Ego4D Con\u00adsor\u00adtium, \u201cEgo\u00adcen\u00adtric 4D Per\u00adcep\u00adtion (Ego4D),\u201d project site. <a href=\"https:\/\/ego4d-data.org\/\" rel=\"noopener\" target=\"_blank\">ego4d-data.org<\/a><\/li>\n\n\n\n<li>Shi et al., \u201cEgo\u00adHu\u00admanoid,\u201d arXiv:2602.10106 (v2, June 2026). <a href=\"https:\/\/arxiv.org\/abs\/2602.10106\" rel=\"noopener\" target=\"_blank\">arxiv.org\/abs\/2602.10106<\/a><\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">About the author<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Graveiens AI Edi\u00adto\u00adr\u00adi\u00adal Team.<\/strong> The Graveiens AI edi\u00adto\u00adr\u00adi\u00adal team writes about human data for AI: ego\u00adcen\u00adtric and robot\u00adics data col\u00adlec\u00adtion, tele\u00adop\u00ader\u00ada\u00adtion, speech data, anno\u00adta\u00adtion, and eval\u00adu\u00ada\u00adtion. Our guides draw on how we scope, cap\u00adture, and review con\u00adsent-based data pro\u00adgrams for AI teams.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Humanoid robot train\u00ading data explained: ego\u00adcen\u00adtric video, tele\u00adop\u00ader\u00ada\u00adtion, sim and mocap com\u00adpared, plus sync specs, con\u00adsent and how to scope a pilot.<\/p>\n","protected":false},"author":1,"featured_media":223,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[4],"tags":[5,15,8,16,17],"class_list":["post-227","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-robotics-physical-ai","tag-egocentric-video","tag-humanoid-robots","tag-physical-ai","tag-robot-training-data","tag-teleoperation"],"_links":{"self":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/227","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=227"}],"version-history":[{"count":1,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/227\/revisions"}],"predecessor-version":[{"id":238,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/227\/revisions\/238"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/media\/223"}],"wp:attachment":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/media?parent=227"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/categories?post=227"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/tags?post=227"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}