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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. 🔗... show more

Xiaomi Tech

3,375 subscribers

23,828 views • 2 months ago •via X (Twitter)

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7 Comments

Xiaomi Tech2 months ago

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.

Xiaomi Tech2 months ago

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.

NovaByte2 months ago

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

luis2 months ago

@Presidentlin

crypto zion2 months ago

And @OfficialXYO is perfect for ai

Samian2 months ago

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

Naidelyn_C.a.r.p2 months ago

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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1,814,925 views • 2 months ago

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17,889 views • 2 months ago

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13,358 views • 1 year ago

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23,681 views • 2 months ago

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283,112 views • 6 months ago

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25,153 views • 1 year ago

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Lukas Ziegler

40,583 views • 6 months ago

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄

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23,392 views • 3 months ago

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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Lukas Ziegler

30,136 views • 3 months ago

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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Y Combinator

90,109 views • 7 months ago

Introducing Midcentury. We’re building the data and simulation infra for physical AI. Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos. We’re already supporting frontier labs with: → The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks → Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale

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Midcentury

632,185 views • 19 days ago

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105,162 views • 4 months ago

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118,117 views • 11 days ago