<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>All 具身智能公司的数据从哪来？开源数据与商业实战数据 - Naifan Li's Blog</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/</link><description>All 具身智能公司的数据从哪来？开源数据与商业实战数据 | Naifan Li's Blog</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><lastBuildDate>Tue, 31 Mar 2026 13:18:51 +0800</lastBuildDate><atom:link href="https://blog.omagiclee.com/posts/embodied-intelligence/data/index.xml" rel="self" type="application/rss+xml"/><item><title>京东具身数据</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/joyegocam/</link><pubDate>Tue, 21 Apr 2026 13:33:45 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/joyegocam/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<p>【26.04.16京东具身智能生态发布会，发布具身大模型JoyAI-RA、具身智能交易平台】https://www.bilibili.com/video/BV1H8dYBTEax?vd_source=e05fafaa995c59e16affc059ba418214</p>]]></description></item><item><title>Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/umi/</link><pubDate>Thu, 16 Apr 2026 19:53:52 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/umi/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i><a href="https://arxiv.org/abs/2402.10329" target="_blank" rel="noopener noreffer ">arXiv 2402.10329</a>
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<i class="fas fa-globe fa-fw" aria-hidden="true"></i><a href="https://umi-gripper.github.io/" target="_blank" rel="noopener noreffer ">Project</a>
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<p>鹿鸣机器人， 遥操，一天采集100-120条数据，人工费200块，一条成本两块钱。</p>
<p>fast umi 一天采集500+，4毛到5毛左右；夹爪成本和机器人本体成本降低，一个本体价格大概在30-40万人民币，</p>]]></description></item><item><title>Index</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/aoe/</link><pubDate>Thu, 16 Apr 2026 14:20:11 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/aoe/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<p></p>
<p>Data collection:</p>
<ul>
<li><strong>Hand-held Gripper-based Interfaces</strong>
<ul>
<li>UMI</li>
<li>FastUMI</li>
<li>DexUMI</li>
</ul>
</li>
<li><strong>Wearable AR/VR Systems</strong>
Humanoidexo: Scalable whole-body humanoid manipulation via wearable exoskeleton</li>
<li><strong>Passive Egocentric Videos</strong></li>
</ul>
<h2 id="always-on-egocentric-data-system">Always-On Egocentric Data System</h2>
<h3 id="hardware">Hardware</h3>
<p></p>
<h3 id="mobile-software">Mobile Software</h3>
<p></p>]]></description></item><item><title>Index</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/shanghai-robot-gov/</link><pubDate>Wed, 15 Apr 2026 11:01:22 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/shanghai-robot-gov/</guid><description>&lt;p>人形机器人具身智能训练场（以下简称“训练场”）由国地中心牵头建设，位于浦东张江模力社区，建设面积超5000平，首期已部署超过100台异构人形机器人，是全国首个异构人形机器人训练场。&lt;/p></description></item><item><title>Index</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/nils/</link><pubDate>Mon, 13 Apr 2026 16:36:41 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/nils/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<h2 id="tldr">TL;DR</h2>
<p><span style="color:red;"></span></p>
<p></p>
<h3 id="identifying-objects-in-the-scene">Identifying Objects in the Scene</h3>
<p>-&gt; objects and properties.</p>
<p>VLMs, Grounding DINO, SigLIP</p>
<h3 id="object-centric-scene-annotation">Object-Centric Scene Annotation</h3>
<p>using an ensemble of open-vocabulary detection and segmentation models.</p>
<h3 id="keystate-detection-and-label-generation">Keystate Detection and Label Generation</h3>
<p></p>]]></description></item><item><title>Robo2VLM</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/robo2vlm/</link><pubDate>Mon, 13 Apr 2026 16:02:27 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/robo2vlm/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<h2 id="tldr">TL;DR</h2>
<p><span style="color:red;"></span></p>
<h2 id="motivations--innovations">Motivations &amp; Innovations</h2>
<h2 id="data-recipe">Data Recipe</h2>
<p></p>
<h3 id="scene-interaction-understanding">Scene-Interaction Understanding</h3>
<p><strong>Embodied Scene Understanding:</strong></p>
<p>Input:</p>
<ul>
<li>a task description in nature language</li>
<li>images from different camera views</li>
</ul>
<p>Output:</p>
<ul>
<li>using an off-the-shelf LLM such as Qwen 2.5 to obtain target object, scene, task and skill description.</li>
</ul>
<p><strong>Manipulation Phase Segmentation:</strong></p>]]></description></item><item><title>DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/droid/</link><pubDate>Mon, 13 Apr 2026 15:20:56 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/droid/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<h2 id="tldr">TL;DR</h2>
<p><span style="color:red;"></span></p>
<h2 id="motivations--innovations">Motivations &amp; Innovations</h2>
<h2 id="approach">Approach</h2>
<h3 id="model">Model</h3>
<h3 id="training-recipe">Training Recipe</h3>
<h3 id="data-recipe">Data Recipe</h3>
<h2 id="experiments">Experiments</h2>]]></description></item><item><title>Open X-Embodiment: Robotic Learning Datasets and RT-X Models</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/oxe/</link><pubDate>Mon, 13 Apr 2026 14:40:49 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/oxe/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<i class="fas fa-globe fa-fw" aria-hidden="true"></i><a href="https://robotics-transformer-x.github.io/" target="_blank" rel="noopener noreffer "></a>
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<h2 id="open-x-embodiment-dataset">Open X-Embodiment Dataset</h2>
<p>robot learning dataset with 1M+ robot trajectories from 22 robot embodiments.</p>
<h3 id="dataset-analysis">Dataset Analysis</h3>
<p></p>
<h2 id="pre-trained-checkpoints">Pre-Trained Checkpoints</h2>
<p>a selection of RT-X model checkpoints ready for inference and finetuning.</p>
<h2 id="tldr">TL;DR</h2>
<p><span style="color:red;"></span></p>
<p></p>]]></description></item><item><title>Index</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/summary/</link><pubDate>Mon, 13 Apr 2026 14:10:49 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/summary/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<p>EgoCentric，iPhone 固定在头上，录制自己叠衣服、洗碗、做饭的画面</p>
<p>Micro1
Scale AI、Encord等数据公司都在招募自己的数据采集大军，DoorDash 则付钱给外卖员拍摄做家务的视频。</p>]]></description></item><item><title>UniACT-dataset</title><link>https://blog.omagiclee.com/posts/embodied-intelligence/data/uniact-dataset/</link><pubDate>Tue, 07 Apr 2026 14:49:10 +0800</pubDate><guid>https://blog.omagiclee.com/posts/embodied-intelligence/data/uniact-dataset/</guid><description><![CDATA[<p><i class="fas fa-award fa-fw" aria-hidden="true"></i><span style="color:gray"></span>
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<i class="fas fa-file-pdf fa-fw" aria-hidden="true"></i>arXiv 
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<p>From six public datasets, we clean, standardize, and balance samples to construct UniACT-dataset, a large-scale dataset with over 6 million trajectories and 9,500 hours of data,</p>
<p></p>
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