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Robots assembling robot brain -- imagine this kind of robustness on every precision manufacturing line! Live demo of GPU rack assembly at #NVIDIAGTC: - end-to-end neural network (Skild Brain) finetuned with little data - memory to perform long horizon task (placing jigs, 16 screwes, removing jigs) - robust to... show more

Deepak Pathak

30,818 subscribers

46,392 views • 6 months ago •via X (Twitter)

Science & Technology #NVIDIAGTC

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13 Comments

Jerry Chéng6 months ago

Super cool! But does it also generalize to other tool use? I feel this demo could also be achieved by non-learning methods using the classical robotics stack. Gripper and rigid attachment may not be good in the long run

Deepak Pathak6 months ago

Classical methods are no where close this kind of randomness and require <0.1mm precision sensing. As for transfer, same base model (Skild Brain) is operating across examples in this thread.

Sherif Zaidan6 months ago

@chris_j_paxton Oh man, universal robot arms and RobotiQ grippers .. feels like 2012 all over again

Deepak Pathak6 months ago

@chris_j_paxton Haha.. unfortunately options are really scarce when you want to deploy something that can last 1000s of hrs without intervention. Do let me know if you have some good alternatives!

JulianSaks6 months ago

super impressive!

Futuro Martinez6 months ago

Robots assembling the very infrastructure powering AI! This precision and robustness with off-the-shelf components is game-changing for manufacturing scalability.

Decent Cloud6 months ago

Robots building GPU racks. The constraint shifted from assembly to allocation.

Pushkar6 months ago

How much real-world finetuning data was needed to reach this level of robustness?

Weijie Wang2 months ago

live long-horizon task on off-the-shelf hardware is a genuine strength, but still an n≈1 capability demo—without success rates and disturbance metrics, "robust" is unearned.

𓅋 𐎫𐎤𐎶 ‎ﷺ6 months ago

What model is this running, was it trained for this specific task?

Deepak Pathak6 months ago

Skild Brain fine-tuned with little domain data. Same base model as all tasks in this thread:

Flexa6 months ago

"Finetuned with little domain data" is the key signal. The base model is doing the heavy lifting — the domain data just steers it. This is the same pattern as LLM fine-tuning. The bottleneck shifts from "how much data" to "what distribution of data" covers the long tail.

EDWARD (❖,❖)6 months ago

This robust robot brain demo looks cool for manufacturing 🦾

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118,965 views • 2 months ago

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36,838 views • 5 months ago

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Here’s the windmill assembly demo we showed at CES 2026 — the one no one saw coming. North executes a fully autonomous, long-horizon dexterous sequence with sustained hand–eye–tactile coordination and assembly-level precision enabled by tactile feedback. It’s also robust to disturbance: you can reposition the objects, and North will still identify them and recover the task. This is powered by CraftNet (VTLA) — using tactile feedback to continuously fine-tune the last-millimeter interaction, enabling reliable execution across 30+ steps. Read more about CraftNet: #Sharpa #SharpaWave #SharpaNorth #CraftNet #System0 #CES2026

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88,304 views • 9 months ago

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20,941 views • 9 months ago

$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

Frank

346,227 views • 22 days ago

Here is a human assembling a BMW engine by hand. Hundreds of components. Micron tolerances. Dozens of hours. So what's better here—a human or a robot❓ 🤖 Robots win at: - Repeatability. Same torque, same angle, every time. - Speed on defined tasks. Honda automates piston installation at 9,000/day. - No fatigue. Shift 47 is identical to shift 1. 👨‍🏭 Humans win at: - Feeling what they can't see. Half of engine assembly is blind. - Force intuition. Knowing "seated" from "about to crack." - Compliant manipulation. That instinctive wiggle to get a part to drop in. - Handling variance. Mercedes pulled robots off the S-Class line because they couldn't adapt to option variability. - Deformables. Wiring harnesses, gaskets, hoses—nothing predictable. - Recovery. When something's wrong, humans adapt. Robots halt. ✅ Right now, the answer is both. Robots handle the repetitive subassemblies. Humans handle everything that requires judgment, feeling, and adaptation. The day a humanoid can do this end-to-end is the day manipulation is solved.

Here is a human assembling a BMW engine by hand. Hundreds of components. Micron tolerances. Dozens of hours. So what's better here—a human or a robot❓ 🤖 Robots win at: - Repeatability. Same torque, same angle, every time. - Speed on defined tasks. Honda automates piston installation at 9,000/day. - No fatigue. Shift 47 is identical to shift 1. 👨‍🏭 Humans win at: - Feeling what they can't see. Half of engine assembly is blind. - Force intuition. Knowing "seated" from "about to crack." - Compliant manipulation. That instinctive wiggle to get a part to drop in. - Handling variance. Mercedes pulled robots off the S-Class line because they couldn't adapt to option variability. - Deformables. Wiring harnesses, gaskets, hoses—nothing predictable. - Recovery. When something's wrong, humans adapt. Robots halt. ✅ Right now, the answer is both. Robots handle the repetitive subassemblies. Humans handle everything that requires judgment, feeling, and adaptation. The day a humanoid can do this end-to-end is the day manipulation is solved.

Jack 🤖

379,855 views • 10 months ago

TESLA’S OPTIMUS JUST CARRIED A $10,000+ GPU LIKE IT WAS A GROCERY BAG! The robot is carrying NVIDIA’s RTX PRO 6000 Blackwell workstation GPU while walking and keeping the package stable in its hands. That sounds simple until you remember what’s happening underneath: 2 legs balancing, 2 arms controlling the load, cameras tracking the environment, and motors constantly making tiny corrections. A task that looks completely ordinary to a human is actually a serious test of real-world robotic control. This is where humanoid robots get interesting. They don’t need to learn 1,000 completely different machines if the same pair of hands can eventually pick up a GPU, move a box, open a door, carry equipment, and perform dozens of other tasks. One general-purpose robot could potentially replace an entire collection of specialized machines. The hardware is impressive, but the bigger story is versatility. The moment a robot can reliably handle arbitrary objects instead of one predefined task, the economics of automation start to change.

TESLA’S OPTIMUS JUST CARRIED A $10,000+ GPU LIKE IT WAS A GROCERY BAG! The robot is carrying NVIDIA’s RTX PRO 6000 Blackwell workstation GPU while walking and keeping the package stable in its hands. That sounds simple until you remember what’s happening underneath: 2 legs balancing, 2 arms controlling the load, cameras tracking the environment, and motors constantly making tiny corrections. A task that looks completely ordinary to a human is actually a serious test of real-world robotic control. This is where humanoid robots get interesting. They don’t need to learn 1,000 completely different machines if the same pair of hands can eventually pick up a GPU, move a box, open a door, carry equipment, and perform dozens of other tasks. One general-purpose robot could potentially replace an entire collection of specialized machines. The hardware is impressive, but the bigger story is versatility. The moment a robot can reliably handle arbitrary objects instead of one predefined task, the economics of automation start to change.

Future Memo

36,252 views • 20 days ago