Satya Nadella’s Post
Introducing Rho-alpha, our newest robotics model from Microsoft Research. Rho-alpha turns natural language into precise control for bimanual robotic manipulation, adding new sensing modalities and learning from human feedback. Looking forward to what’s to come as we bring physical AI into the real world. https://lnkd.in/g5pYPn5f
[Introducing Rho-alpha, the new robotics model from Microsoft https://www.microsoft.com/en-us/research ](https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Emicrosoft%2Ecom%2Fen-us%2Fresearch%2Fstory%2Fadvancing-ai-for-the-physical-…
Satya Nadella’s Post
Introducing Rho-alpha, our newest robotics model from Microsoft Research. Rho-alpha turns natural language into precise control for bimanual robotic manipulation, adding new sensing modalities and learning from human feedback. Looking forward to what’s to come as we bring physical AI into the real world. https://lnkd.in/g5pYPn5f
Translating language in to reliable physical action is a major step toward real world AI. Human feedback and precision will be critical as physical AI moves from research to deployment
Impressive progress. Bridging language and physical action is what will make AI truly useful in the real world. Looking forward to the practical applications this unlocks.
Finally I can attach robotic arms and legs to my Zune
This will surely help in the workshop organisation for companies having large factories and goods in huge amounts. Microsoft is purely transforming itself by such changes !
The real breakthrough isn’t robotics; it’s translation. Turning intent into reliable action under uncertainty is the same challenge leaders face every day.
This is the robotic work of the world 🌎
Nadella’s robot post points at a future where intelligence depends on shared, contextual state. The Fragmental Overlap Storage System (FOSS) addresses that same requirement at the storage layer — deterministically — without relying on probabilistic or exotic hardware. — Cecil A. Lacy Inventor of the Fragmental Overlap Storage System (FOSS) & Fragmental Network Protocol (FNP) Patent Pending: 19/264,676
This is a big deal. Natural language is cheap. Reliable manipulation is the hard part. Bimanual control + new sensing + learning from human feedback is basically the recipe for moving from “cool demo” to “useful in the messy world.” Interested what you see as the first real breakout use case. Warehouse kitting, light manufacturing, or lab/healthcare workflows where precision matters and variance is constant?
Advances like Rho-alpha make one thing clear: the next frontier isn’t just the model, but the environment where that model is built, tested, and constrained. Physical AI can’t emerge from spaces designed for SaaS teams. It requires infrastructure built for hardware/software integration, safe experimentation, traceability, and real human feedback. Designing the physical layer before computation is the quiet challenge of deep tech. Without it, physical AI remains stuck in the lab.
This could be a major unlock for manufacturing, logistics, and assistive robotics especially when tasks need flexible, natural instruction.
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