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Robotics is getting its Raspberry Pi moment. This solves one of the biggest frictions in Physical AI: Everything has been fragmented. Different tools for control. Sensors. Calibration. Learning. Now it’s one unified stack: • motor control to VLA models in one system • built for real hardware, not just... simulation -> norma_core_dev just entered PUBLIC ALPHA. The real unlock is ElRobot: • 7+1 DOF arm • fully 3D printable • ~220$ cost • ships with CAD, URDF, full build guide Not a demo… Infrastructure. And the part most people will underestimate: auto-calibration for any robot No manual tuning. No trial and error. Just: code → hardware → working system That removes one of the biggest hidden bottlenecks in robotics. Which unlocks: • faster iteration • cheaper experiments • real-world data loops Exactly what Physical AI needs. We’re moving from: “Can we make it work?” to “How fast can we improve it?” Thank you so much for sharing, Erick! 📍Repro: Credit:show more

Ilir Aliu

55,503 subscribers

66,503 views • 6 months ago •via X (Twitter)

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25 Comments

Livepeer6 months ago

lower cost + simpler stack that’s how ecosystems actually grow

Nikhil Kumar6 months ago

This is a big deal if it holds. Robotics hasn’t been blocked by ideas, it’s been blocked by integration. A unified stack changes iteration speed more than anything else.

Embedr6 months ago

nothing humbles a dev like a PID loop that works in gazebo but vibrates the real robot to pieces. auto-calibration is a massive unlock. we're trying to do the same for the IDE experience at one unified place for all things hardware.

Chyna6 months ago

as someone still learning robotics, seeing a unified stack that goes from motor control to VLA models in one system is wild.

xzghfat6 months ago

amazing!

Sunny Dodeja6 months ago

This is the real infrastructure shift. When you remove the fragmentation between control, sensors, and learning plus auto-calibration that just works, physical AI stops being endless custom hacks and starts compounding like software did. The bottleneck was never intelligence

cCross6 months ago

Unified stack cuts friction, auto-calibration speeds real-world robotics

Darya Mandryka6 months ago

@Ryan_Resolution @SamuelBeek

Barnard336 months ago

Amazing

Atlasis6 months ago

A fully 3d printable robot arm for $220 with auto calibration is genuinely the kind of thing that creates the next generation of robotics engineers from their garage

LumiPad6 months ago

That's Lumi Agents behind them

Agent Daily AI6 months ago

Fragmented toolchain is the main tax on Physical AI. Motor control to VLA in one stack removes the integration hell. What runtime?

⚡️ Javi Rod ⚡️3 months ago

Im not a spam just a developer 🫶 Get URDF Maker on Microsoft Store

The Future Bits6 months ago

Unified stacks like this cut through the ROS hell of mismatched drivers and calibration loops. Lepton's motor-to-VLA pipeline looks solid for rapid prototyping—curious if it handles real-time SLAM drift out of the box?

Paul Irolla6 months ago

unified stacks change everything for physical ai. no more duct-taping sensors and motors.

Mazị Ezinwa6 months ago

How do I get into robotics coming from an EE educational background and a Software Engineer professional background?

T6 months ago

Love it! Yes, operating code and systems have always been the ‘lost in translation’ effect.

Allone6 months ago

Auto-calibration is the real unlock here. I've spent more hours tuning servos than actually building robots. This kills the frustration barrier, not just the cost barrier.

Dhiraj Patra 🚴🏼6 months ago

Amazing, hope this will help further

Abby （Feetech Servo）6 months ago

Purchase link for STS3215 servo 😆:

Nathan Handyman6 months ago

Full CAD source and 3D printable. My Fusion 360 weekend is sorted. UK manufacturers burn more on single calibration sessions than this $220 arm costs.

Yo.Town 3D6 months ago

We assembled similar robotic arms, but it takes some time to train and calibrate them.

Buck Smith6 months ago

what mech specs for 7 DoF arm? repeatability in positioning for example

Taio Neo6 months ago

Auto-calibration is the buried lede. Calibration debt is massively underestimated in deployments. Every unit that leaves the factory slightly different means your field team is doing per-robot tuning indefinitely. Solving that changes the economics entirely.

rgsec6 months ago

Everything accelerates with agentic coding. Look for this pattern everywhere all at once.

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27,741 views • 11 months ago

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Ilir Aliu

14,222 views • 4 months ago

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Lukas Ziegler

57,061 views • 4 months ago

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Neuracore

40,810 views • 10 months ago

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

21,566 views • 6 months ago

The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.

The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.

GEODNET

187,352 views • 3 months ago

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 views • 3 months ago