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Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗...

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23,828 görüntüleme • 2 ay önce •via X (Twitter)

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Key findings: • Scaling data and model size consistently improve model performance during pre-training. • Scaling of pre-training transfers to strong out-of-the-box performance in unseen environments + efficient adaptation to novel challenging tasks. • #1 on RoboCasa365: 57.4% average success rate (vs. previous best 46.6%), with particularly strong generalization on the Composite-Unseen split. • #1 on RoboDojo: 20.07 average score & 13.93% success rate (vs. previous 13.07 & 8.80%). • 74.5% success rate on RoboCasa and 59.1% success rate on VLABench, outperforming RLDX-1, Cosmos Policy, GR00T N1.6, Pi-0.5, and Pi-0-FAST.

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Scaling matters only if it transfers to real robots. We evaluate Xiaomi-Robotics-1 out-of-the-box on real-world mobile manipulation tasks -- including shoe organization, bag packing, table organization, and sofa tidying -- in unseen environments. Results showcase that scaling both pre-training data and model size consistently leads to better out-of-the-box performance in these real-robot evaluations. Furthermore, Xiaomi-Robotics-1 serves as a strong robot foundation model capable of adapting to novel tasks with minimal downstream data. Code and model checkpoints will be released soon.

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NovaByte2 ay önce

1/3 🧹 Tozlar, saçlar, kırıntılar derken temizliğe hiç vakit kalmıyor değil mi? Xiaomi Robot Vacuum H50 Pro imdadınıza yetişiyor! Güçlü emişi ve akıllı haritalama ile evin her köşesini saniyeler içinde tertemiz yapar. Siz kahvenizi yudumlarken o çalışır! #Xiaomi #teknoloji

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@Presidentlin

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And @OfficialXYO is perfect for ai

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100k hours of real manipulation data is wild. most people don't realize how hard that is to collect

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Naidelyn_C.a.r.p2 ay önce

El mundo observa. Las empresas globales tienen la oportunidad de ser parte de la solución. Soliciten al gobierno chino leyes que protejan a los animales y castiguen la crueldad. El verdadero progreso también se mide por la empatía y la justicia. #EndAnimalCrueltyInChina

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Xiaomi Robotics-1 🤖a new robot foundation model from Xiaomi Tech - 100K+ hours real world robot data - VLA architecture - Cross-embodiment generalization - Fast adaptation to new tasks
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Xiaomi Robotics-1 🤖a new robot foundation model from Xiaomi Tech - 100K+ hours real world robot data - VLA architecture - Cross-embodiment generalization - Fast adaptation to new tasks

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That's one of the coolest robotics demos! 😮‍💨 Generalist showed GEN-1 handling box folding and screw packing during Automate Show. The boxes are cardboard with real variability: creasing, deformation, different configurations. GEN-1 retries when things go wrong. It adapts mid-task. This is a jump from GEN-0, shown 3 months ago during GTC. GEN-0 handled rigid, predictable boxes. GEN-1 handles deformable ones with chaotic behavior. The scaling is straightforward. GEN-1 trained on 500,000 hours of proprietary, real-world dexterous manipulation data. Scaling laws are working. More high-quality real-world data leads to better physics understanding, better retry behavior, better generalization. And the time to get a working demo is shrinking drastically. They're not even at scale yet. They're just starting to see what happens when you pre-train on massive amounts of actual robot data. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Origami Robotics is building high-DOF robotic hands with in-joint motors and a co-designed data-collection glove to eliminate the embodiment gap by collecting high-quality, real-world data at scale. Congrats on the launch, Quanting Xie and Ryan Xie!

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1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group Zhuoyuan Wu! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: 🌐 Project: 💻 Code: 🤗 Robot data: 🤗 Human data: 🤗 Weights: #Robotics #WorldModels #AI #OpenSource
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1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group Zhuoyuan Wu! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: 🌐 Project: 💻 Code: 🤗 Robot data: 🤗 Human data: 🤗 Weights: #Robotics #WorldModels #AI #OpenSource

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Introducing Praxis-1, an open-weight World Action Model that turns our video pre-training expertise into real world control for robots. Robot demonstration data is limited. But video data is infinite. Praxis-1 extends our bet that the best policy models will learn from video, and use that scale to bring embodied intelligence to every industry. Praxis-1 is built on our large-scale video pre-training and real-time infrastructure, and acts as a policy model for robotics developers and researchers that works across any embodiment or environment, no matter how complex. We are actively testing with early partners including Noble Machines, Standard Bots and Ultra, and will be releasing the weights publicly in the coming months. Learn more and request early access at

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