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Tesla vehicles and Optimus share core technology: Actuators Power electronics Battery Manufacturing Data communication Audio system Cameras A14/A15 chips Training cluster Neural simulation Real-world AI

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The Humanoid Hub

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19,499 просмотров • 8 месяцев назад •via X (Twitter)

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One of the most interesting parts of our interview with Tesla VP Lars Moravy wasn't just about Optimus, it was why Tesla thinks it can win! Tesla VP Lars said Tesla's biggest advantages are manufacturing at scale, designing high-performance actuators, and using real-world AI trained on millions of hours of data. He also shared why Tesla started with a humanoid design. People naturally trust something that looks familiar, making it easier to introduce robots into everyday life before exploring other designs. It's a reminder that building a great robot isn't just about AI. It's also about engineering, manufacturing, and getting people to accept it. $TSLA Jeff Lutz 🔋
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One of the most interesting parts of our interview with Tesla VP Lars Moravy wasn't just about Optimus, it was why Tesla thinks it can win! Tesla VP Lars said Tesla's biggest advantages are manufacturing at scale, designing high-performance actuators, and using real-world AI trained on millions of hours of data. He also shared why Tesla started with a humanoid design. People naturally trust something that looks familiar, making it easier to introduce robots into everyday life before exploring other designs. It's a reminder that building a great robot isn't just about AI. It's also about engineering, manufacturing, and getting people to accept it. $TSLA Jeff Lutz 🔋

Herbert Ong

94,606 просмотров • 12 дней назад

ELON MUSK: 2026 is going to be great for Tesla. • Massive production expansion across multiple factories and five major new production lines • Cybercab production starting in Texas, scaling through the year, with potential European manufacturing • Tesla Semi ramping up, heavy truck expansion targeting Europe next year • Full Self Driving rollout accelerating, with expected approval in the Netherlands and broader Europe • Goal of achieving real world AI so advanced you could sleep in the car and wake up at your destination • Optimus humanoid robot program advancing toward real world use cases • Optimus positioned to help with daily tasks like childcare, walking the dog, and elderly care • In house battery cell manufacturing scaling at Gigafactory level • Tesla lithium refinery operational in Texas • Tesla nickel cathode refinery running in Austin • Significant battery cell production ramp throughout the year • Potential expansion of Cybercab and Optimus manufacturing into Europe
3:36

ELON MUSK: 2026 is going to be great for Tesla. • Massive production expansion across multiple factories and five major new production lines • Cybercab production starting in Texas, scaling through the year, with potential European manufacturing • Tesla Semi ramping up, heavy truck expansion targeting Europe next year • Full Self Driving rollout accelerating, with expected approval in the Netherlands and broader Europe • Goal of achieving real world AI so advanced you could sleep in the car and wake up at your destination • Optimus humanoid robot program advancing toward real world use cases • Optimus positioned to help with daily tasks like childcare, walking the dog, and elderly care • In house battery cell manufacturing scaling at Gigafactory level • Tesla lithium refinery operational in Texas • Tesla nickel cathode refinery running in Austin • Significant battery cell production ramp throughout the year • Potential expansion of Cybercab and Optimus manufacturing into Europe

DogeDesigner

98,894 просмотров • 4 месяцев назад

The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥
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The next frontier of AI is physical. 🤖 From simulation to synthetic data, developers are training robots to understand the real world with NVIDIA Cosmos, Isaac simulation frameworks and open-source physics engine, Newton. Watch the video to see how partners like Hon Hai Technology Group (Foxconn), Hexagon, Humanoid, Noble Machines, and Skild AI are building the future. 🎥

NVIDIA Robotics

21,664 просмотров • 3 месяцев назад

AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.
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AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.

The Humanoid Hub

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You asked for it, we delivered. The full keynote is live. In this keynote, we unveil our enhanced vision to become the World’s Ears for AI & Robotics. We are creating the world’s first decentralized infrastructure for real-world audio intelligence. Check-out our new website: Start training Voice AI now & get rewarded: From noise-level mapping to multilingual voice data and the future of live audio streams, Silencio is building the critical audio data layer that will power foundation models, embodied AI, autonomous vehicles, and robotics.
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You asked for it, we delivered. The full keynote is live. In this keynote, we unveil our enhanced vision to become the World’s Ears for AI & Robotics. We are creating the world’s first decentralized infrastructure for real-world audio intelligence. Check-out our new website: Start training Voice AI now & get rewarded: From noise-level mapping to multilingual voice data and the future of live audio streams, Silencio is building the critical audio data layer that will power foundation models, embodied AI, autonomous vehicles, and robotics.

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It’s not going to be easy competing against Tesla’s Optimus. Just like no one’s been able to match Tesla’s FSD, this will be the same story. Only this time, it’s going to be even harder. Bc Tesla has everything integrated from AI training to hardware to real-world data, and Optimus is powered by the same FSD tech that’s already mastering real-world autonomy. No other company is this vertically integrated, it’s just that Tesla makes it look easy.
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It’s not going to be easy competing against Tesla’s Optimus. Just like no one’s been able to match Tesla’s FSD, this will be the same story. Only this time, it’s going to be even harder. Bc Tesla has everything integrated from AI training to hardware to real-world data, and Optimus is powered by the same FSD tech that’s already mastering real-world autonomy. No other company is this vertically integrated, it’s just that Tesla makes it look easy.

Teslaconomics

99,840 просмотров • 8 месяцев назад

ELON: TESLA IS UNIQUELY POSITIONED TO MANUFACTURE A HUMANOID ROBOT “We really have by far the best team of humanoid robotics engineers in the world. We also have all the other ingredients necessary. You need a great battery pack, you need great power electronics, you need a great charging capability, you need great communications, it's going to have Wi-Fi and cellular connectivity. And of course you need real-world AI. And then, the ability to scale that production to huge levels. You have to design for manufacturing. What other companies are missing is they're missing the real-world AI, and they're missing the ability to scale manufacturing to millions of units a year.” Source: Tesla Financial Results Webcast, January 29, 2025
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ELON: TESLA IS UNIQUELY POSITIONED TO MANUFACTURE A HUMANOID ROBOT “We really have by far the best team of humanoid robotics engineers in the world. We also have all the other ingredients necessary. You need a great battery pack, you need great power electronics, you need a great charging capability, you need great communications, it's going to have Wi-Fi and cellular connectivity. And of course you need real-world AI. And then, the ability to scale that production to huge levels. You have to design for manufacturing. What other companies are missing is they're missing the real-world AI, and they're missing the ability to scale manufacturing to millions of units a year.” Source: Tesla Financial Results Webcast, January 29, 2025

Mario Nawfal

1,336,887 просмотров • 1 год назад

Today on Training Data: Sanjit Biswas, founder & CEO of Samsara (NYSE:IOT) and former Sequoia Capital backed founder of Cisco Meraki Sanjit shares the ups & downs of running neural nets on constrained compute and power footprints in the real world, ~2-10 watts Physical AI is hard 🫡
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Today on Training Data: Sanjit Biswas, founder & CEO of Samsara (NYSE:IOT) and former Sequoia Capital backed founder of Cisco Meraki Sanjit shares the ups & downs of running neural nets on constrained compute and power footprints in the real world, ~2-10 watts Physical AI is hard 🫡

Sonya Huang 🐥

35,714 просмотров • 6 месяцев назад

🧵 THREAD: Compilation of Tesla Robotaxi clips. As I compile this list, I have to restrain myself from going on Tesla 's website to buy one ☺️ 1. Tesla Robotaxi slows for guy getting arrested by police. Tesla's robotaxi, built on its Full Self-Driving (FSD) technology, primarily relies on a vision-based system using cameras, neural networks, and AI to sense and identify vehicles like police cars and school buses. Per WholeMarsBlog: "The more I use Robotaxi the more impressed I am." Credit: Whole Mars Catalog
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🧵 THREAD: Compilation of Tesla Robotaxi clips. As I compile this list, I have to restrain myself from going on Tesla 's website to buy one ☺️ 1. Tesla Robotaxi slows for guy getting arrested by police. Tesla's robotaxi, built on its Full Self-Driving (FSD) technology, primarily relies on a vision-based system using cameras, neural networks, and AI to sense and identify vehicles like police cars and school buses. Per WholeMarsBlog: "The more I use Robotaxi the more impressed I am." Credit: Whole Mars Catalog

Barbie True Blue

110,669 просмотров • 1 год назад

NEWS: Nvidia shows how its new “Cosmos” system uses cars to generate synthetic real world driving scenarios to train self-driving cars. It can create drivable 3D environments. Driving scenario variations can be generated from replayed drive logs or AI traffic generators. Next, a neural reconstruction engine uses autonomous vehicle sensor logs to create high fidelity 4D simulation environments. It replays previous drives in 3D and generates scenario variations to amplify training data. Nvidia’s AI data factory scales hundreds of drives into billions of effective miles.
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NEWS: Nvidia shows how its new “Cosmos” system uses cars to generate synthetic real world driving scenarios to train self-driving cars. It can create drivable 3D environments. Driving scenario variations can be generated from replayed drive logs or AI traffic generators. Next, a neural reconstruction engine uses autonomous vehicle sensor logs to create high fidelity 4D simulation environments. It replays previous drives in 3D and generates scenario variations to amplify training data. Nvidia’s AI data factory scales hundreds of drives into billions of effective miles.

Sawyer Merritt

918,239 просмотров • 1 год назад

Elon Musk revealed the 3 hardest challenges for humanoid robots and how Tesla is solving them There are really only three hard things for humanoid robots: - Real-world intelligence - ⁠The hand and - ⁠Scale manufacturing "I haven’t seen any demo robots that have a great hand with all the degrees of freedom of a human hand. But Optimus does have that" To reach human-level dexterity, Tesla had to design custom actuators, motors, gears, power electronics, controls, and sensors, everything from physics first principles There is no supply chain for this "From an electromechanical standpoint, the hand is more difficult than everything else combined. The human hand turns out to be quite something" This is why Optimus is on a completely different level. Tesla is solving the hardest problems from the ground up
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Elon Musk revealed the 3 hardest challenges for humanoid robots and how Tesla is solving them There are really only three hard things for humanoid robots: - Real-world intelligence - ⁠The hand and - ⁠Scale manufacturing "I haven’t seen any demo robots that have a great hand with all the degrees of freedom of a human hand. But Optimus does have that" To reach human-level dexterity, Tesla had to design custom actuators, motors, gears, power electronics, controls, and sensors, everything from physics first principles There is no supply chain for this "From an electromechanical standpoint, the hand is more difficult than everything else combined. The human hand turns out to be quite something" This is why Optimus is on a completely different level. Tesla is solving the hardest problems from the ground up

X Freeze

14,573 просмотров • 4 месяцев назад

A new 30-minute presentation from Ashok Elluswamy, Tesla’s VP of AI, has been released, where he talks about FSD, AI and the team’s latest progress. Highlight from the presentation: • Tesla's vehicle fleet can provide 500 years of driving data every single day. Curse of Dimensionality: • 8 cameras at high frame rate = billions of tokens per 30 seconds of driving context. • Tesla must compress and extract the right correlations between sensory input and control actions. Data Advantage: • Tesla has access to a “Niagara Falls of data” — hundreds of years’ worth of collective fleet driving. • Uses smart data triggers to capture rare corner cases (e.g., complex intersections, unpredictable behavior). Quality and Efficiency: • Extracts only the essential data needed to train models efficiently. Debugging and Interpretability: • Even though the system is end-to-end, Tesla can still prompt the model to output interpretable data: 3D occupancy, road boundaries, objects, signs, traffic lights, etc. • Natural language querying: ask the model why it made a certain decision. • These auxiliary predictions don’t drive the car but help engineers debug and ensure safety. Tesla’s Advanced Gaussian Splatting (3D Scene Modeling): • Tesla developed a custom, ultra-fast Gaussian splatting system to reconstruct 3D scenes from limited camera views. • Produces crisp, accurate 3D renderings even from few camera angles — far better than standard NeRF/splatting approaches. • Enables rapid visual debugging of the driving environment in 3D. Evaluation & World Models: • Evaluation is the hardest challenge: models may perform well offline but fail in real-world conditions. • Tesla builds balanced, diverse evaluation datasets focusing on edge cases — not just easy highway driving. Introduced a learned world simulator (neural network-generated video engine): • Can simulate 8 Tesla camera feeds simultaneously — fully synthetic. • Used for testing, training, and reinforcement learning. • Allows adversarial event injection (e.g., adding a pedestrian or vehicle cutting in). • Enables replaying past failures to verify new model improvements. • Can run in near real-time, letting testers “drive” inside a simulated world. What’s Next: • Scale robotaxi service globally. • Unlock full autonomy across the entire Tesla fleet. • Cybercab: next-gen 2-seat vehicle designed specifically for robotaxi use, targeting lowest transportation cost (cheaper than public transit). • Same neural networks will power Optimus humanoid robot. • The same video generation system is now being applied to Optimus. • The system can simulate and plan movement for robots, adapting easily to new forms. via the International Conference on Computer Vision (ICCV). Full presentation:
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A new 30-minute presentation from Ashok Elluswamy, Tesla’s VP of AI, has been released, where he talks about FSD, AI and the team’s latest progress. Highlight from the presentation: • Tesla's vehicle fleet can provide 500 years of driving data every single day. Curse of Dimensionality: • 8 cameras at high frame rate = billions of tokens per 30 seconds of driving context. • Tesla must compress and extract the right correlations between sensory input and control actions. Data Advantage: • Tesla has access to a “Niagara Falls of data” — hundreds of years’ worth of collective fleet driving. • Uses smart data triggers to capture rare corner cases (e.g., complex intersections, unpredictable behavior). Quality and Efficiency: • Extracts only the essential data needed to train models efficiently. Debugging and Interpretability: • Even though the system is end-to-end, Tesla can still prompt the model to output interpretable data: 3D occupancy, road boundaries, objects, signs, traffic lights, etc. • Natural language querying: ask the model why it made a certain decision. • These auxiliary predictions don’t drive the car but help engineers debug and ensure safety. Tesla’s Advanced Gaussian Splatting (3D Scene Modeling): • Tesla developed a custom, ultra-fast Gaussian splatting system to reconstruct 3D scenes from limited camera views. • Produces crisp, accurate 3D renderings even from few camera angles — far better than standard NeRF/splatting approaches. • Enables rapid visual debugging of the driving environment in 3D. Evaluation & World Models: • Evaluation is the hardest challenge: models may perform well offline but fail in real-world conditions. • Tesla builds balanced, diverse evaluation datasets focusing on edge cases — not just easy highway driving. Introduced a learned world simulator (neural network-generated video engine): • Can simulate 8 Tesla camera feeds simultaneously — fully synthetic. • Used for testing, training, and reinforcement learning. • Allows adversarial event injection (e.g., adding a pedestrian or vehicle cutting in). • Enables replaying past failures to verify new model improvements. • Can run in near real-time, letting testers “drive” inside a simulated world. What’s Next: • Scale robotaxi service globally. • Unlock full autonomy across the entire Tesla fleet. • Cybercab: next-gen 2-seat vehicle designed specifically for robotaxi use, targeting lowest transportation cost (cheaper than public transit). • Same neural networks will power Optimus humanoid robot. • The same video generation system is now being applied to Optimus. • The system can simulate and plan movement for robots, adapting easily to new forms. via the International Conference on Computer Vision (ICCV). Full presentation:

Sawyer Merritt

1,286,614 просмотров • 8 месяцев назад

Elon just dropped a MAJOR nugget on how Tesla is going to be training Optimus to do real world tasks. They are building an Optimus Academy, which is a large scale, dedicated real-world training facility to accelerate the development of Optimus. The Academy will deploy thousands of Optimus units, potentially 10,000 to 30,000 robots, in a controlled realistic environment where they perform self-play, experiment with tasks, iterate on behaviors, and continuously generate training data through trial and error. The Tesla bots will also run millions of simulations in Tesla’s high-fidelity physics-accurate engine, allowing Optimus to close the “sim-to-real gap” by using these real-world observations to refine and validate the simulations! “You’re actually highlighting an important limitation and difference from cars. We’ll soon have 10 million cars on the road. It’s hard to duplicate that massive training flywheel. For the robot, what we’re going to need to do is build a lot of robots and put them in kind of an Optimus Academy so they can do self-play in reality. We’re actually building that out. We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks. Tesla has quite a good reality generator, a physics-accurate reality generator, that we made for the cars. We’ll do the same thing for the robots. We actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. You can do millions of simulated robots in the simulated world. You use the tens of thousands of robots in the real world to close the simulation to reality gap. Close the sim-to-real gap.”
1:20

Elon just dropped a MAJOR nugget on how Tesla is going to be training Optimus to do real world tasks. They are building an Optimus Academy, which is a large scale, dedicated real-world training facility to accelerate the development of Optimus. The Academy will deploy thousands of Optimus units, potentially 10,000 to 30,000 robots, in a controlled realistic environment where they perform self-play, experiment with tasks, iterate on behaviors, and continuously generate training data through trial and error. The Tesla bots will also run millions of simulations in Tesla’s high-fidelity physics-accurate engine, allowing Optimus to close the “sim-to-real gap” by using these real-world observations to refine and validate the simulations! “You’re actually highlighting an important limitation and difference from cars. We’ll soon have 10 million cars on the road. It’s hard to duplicate that massive training flywheel. For the robot, what we’re going to need to do is build a lot of robots and put them in kind of an Optimus Academy so they can do self-play in reality. We’re actually building that out. We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks. Tesla has quite a good reality generator, a physics-accurate reality generator, that we made for the cars. We’ll do the same thing for the robots. We actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. You can do millions of simulated robots in the simulated world. You use the tens of thousands of robots in the real world to close the simulation to reality gap. Close the sim-to-real gap.”

Teslaconomics

42,563 просмотров • 5 месяцев назад

Data has always been the bottleneck for physical AI in self driving and robotics. Tesla is taking two very different approaches for FSD and Optimus. Tesla’s Optimus Training Playbook: 1. Build 30k Optimus Gen 3 robots 2. Operate them in a mock environment where they can perform self-play “Optimus Academy” 3. Train in sim using the real robot data to close sim2real gap. Tesla FSD Training Playbook: 1. Sell millions of cars outfitted with cheap cameras 2. Collect diverse real world driving data (especially intervention and failure recovery data) for free as a byproduct of customers driving the cars. 3. Use driving data to train Autopilot/FSD and deploy policies incrementally as a supervised FSD product 4. Repeat until policy reaches robust unsupervised full self driving for robotaxi launch. The Tesla FSD playbook is a beautiful self-funding, customer subsidized, diverse real world data flywheel. The Optimus playbook is the opposite and shares none of the beautiful attributes of the FSD training flywheel that made FSD successful. The key differences: 1. Instead of having customers pay you for vehicles, Tesla will need to fund 30,000 Optimus robots. Assuming the current landed cost per unit is $100k, that will be $3B to build plus another ~30% per year for maintenance labor and spare parts given it’s still an unhardened pre-production prototype is another $900M per year. For reference, Tesla’s GAAP net income in 2025 was $3.8B. 2. Instead of having customers drive their Teslas on roads all across the world giving Tesla an insanely rich and diverse dataset that Waymo and other AV companies could never collect, the Optimus Academy is doing the equivalent of building a fake town in a parking lot and driving their car in that parking lot. No matter how real you try to make the environments for self-play you can never replicate the diversity, complexity and failure modes of the real world. Data collected in staged environments produces demo-grade policies and will not be rich enough to generalize to the vast diversity of environments, tasks, objects, etc. out of distribution. 3. Instead of having customers collect real world failure recovery data (DAgger style) for free every time FSD disengages, the Optimus Academy will need paid teleoperators or onsite operators to collect the recovery data. Assuming 1 person can manage 2 robots to start that would cost $3.5B in labor per year (30,000 robots, $40/hr fully loaded, 16 hrs/day, 365 days per year, 2:1 robot:operator). Tesla can come up with the money to do this but money doesn’t solve the “mock data” problem. Given the higher degrees of freedom in humanoids vs. cars, training a generalized humanoid will be harder and require more data than a self-driving vehicle. The best way to train your robot is by deploying them in the diverse real world, subsidized by real customer operations. Humanoids face a chicken and egg where it’s very hard to bootstrap your way to a first policy that’s good enough to deploy in real production environments. This is an extremely capital intensive playbook (which doesn’t even include cost of training). Time will tell if it works but a better playbook would be finding a way to copy the FSD playbook.
1:54

Data has always been the bottleneck for physical AI in self driving and robotics. Tesla is taking two very different approaches for FSD and Optimus. Tesla’s Optimus Training Playbook: 1. Build 30k Optimus Gen 3 robots 2. Operate them in a mock environment where they can perform self-play “Optimus Academy” 3. Train in sim using the real robot data to close sim2real gap. Tesla FSD Training Playbook: 1. Sell millions of cars outfitted with cheap cameras 2. Collect diverse real world driving data (especially intervention and failure recovery data) for free as a byproduct of customers driving the cars. 3. Use driving data to train Autopilot/FSD and deploy policies incrementally as a supervised FSD product 4. Repeat until policy reaches robust unsupervised full self driving for robotaxi launch. The Tesla FSD playbook is a beautiful self-funding, customer subsidized, diverse real world data flywheel. The Optimus playbook is the opposite and shares none of the beautiful attributes of the FSD training flywheel that made FSD successful. The key differences: 1. Instead of having customers pay you for vehicles, Tesla will need to fund 30,000 Optimus robots. Assuming the current landed cost per unit is $100k, that will be $3B to build plus another ~30% per year for maintenance labor and spare parts given it’s still an unhardened pre-production prototype is another $900M per year. For reference, Tesla’s GAAP net income in 2025 was $3.8B. 2. Instead of having customers drive their Teslas on roads all across the world giving Tesla an insanely rich and diverse dataset that Waymo and other AV companies could never collect, the Optimus Academy is doing the equivalent of building a fake town in a parking lot and driving their car in that parking lot. No matter how real you try to make the environments for self-play you can never replicate the diversity, complexity and failure modes of the real world. Data collected in staged environments produces demo-grade policies and will not be rich enough to generalize to the vast diversity of environments, tasks, objects, etc. out of distribution. 3. Instead of having customers collect real world failure recovery data (DAgger style) for free every time FSD disengages, the Optimus Academy will need paid teleoperators or onsite operators to collect the recovery data. Assuming 1 person can manage 2 robots to start that would cost $3.5B in labor per year (30,000 robots, $40/hr fully loaded, 16 hrs/day, 365 days per year, 2:1 robot:operator). Tesla can come up with the money to do this but money doesn’t solve the “mock data” problem. Given the higher degrees of freedom in humanoids vs. cars, training a generalized humanoid will be harder and require more data than a self-driving vehicle. The best way to train your robot is by deploying them in the diverse real world, subsidized by real customer operations. Humanoids face a chicken and egg where it’s very hard to bootstrap your way to a first policy that’s good enough to deploy in real production environments. This is an extremely capital intensive playbook (which doesn’t even include cost of training). Time will tell if it works but a better playbook would be finding a way to copy the FSD playbook.

Simon Kalouche

34,671 просмотров • 5 месяцев назад

🚨Meet IRON: China’s Humanoid Robot. So Real People Thought It Was Human 🇨🇳🔥 Meet IRON, XPeng’s next-gen humanoid, unveiled at AI Day in Guangzhou. So lifelike that people swore it was a person in a suit. It wasn’t. That illusion? Brilliant marketing. It forced the world to pay attention. Here’s what makes IRON a game-changer 👇 • 82 degrees of freedom, synthetic muscle system and bionic skin so realistic it moves like flesh. • Runs on XPeng’s in-house AI chips with over 2,000 TOPS computing power. • Solid-state battery, self-balancing gait and a neural control system modelled on the human spine. • Aiming for mass production in 2026, not a fantasy, a roadmap. While others like Tesla’s Optimus still struggle to pick up a box, China’s IRON walks, gestures and interacts naturally, powered by real-time embodied AI. Western media called it “too real,” “creepy,” even “fake.” But in reality, it’s China’s physical AI frontier, bridging robotics, neural networks and mobility tech in a way the West hasn’t caught up to yet. China is not just in the AI race anymore. It’s building the finish line. 🏁🇨🇳
0:40

🚨Meet IRON: China’s Humanoid Robot. So Real People Thought It Was Human 🇨🇳🔥 Meet IRON, XPeng’s next-gen humanoid, unveiled at AI Day in Guangzhou. So lifelike that people swore it was a person in a suit. It wasn’t. That illusion? Brilliant marketing. It forced the world to pay attention. Here’s what makes IRON a game-changer 👇 • 82 degrees of freedom, synthetic muscle system and bionic skin so realistic it moves like flesh. • Runs on XPeng’s in-house AI chips with over 2,000 TOPS computing power. • Solid-state battery, self-balancing gait and a neural control system modelled on the human spine. • Aiming for mass production in 2026, not a fantasy, a roadmap. While others like Tesla’s Optimus still struggle to pick up a box, China’s IRON walks, gestures and interacts naturally, powered by real-time embodied AI. Western media called it “too real,” “creepy,” even “fake.” But in reality, it’s China’s physical AI frontier, bridging robotics, neural networks and mobility tech in a way the West hasn’t caught up to yet. China is not just in the AI race anymore. It’s building the finish line. 🏁🇨🇳

James Wood 武杰士

103,105 просмотров • 8 месяцев назад

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:48

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

22,482 просмотров • 4 месяцев назад

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 просмотров • 3 месяцев назад

"Optimus will be the biggest product ever." - Elon Musk AND THIS IS HOW HE'S GOING TO DO IT.... REDEFINING LABOR ON EARTH The core mission of Optimus is to take over dangerous, repetitive, and monotonous work, making advanced automation accessible to everyone and fundamentally changing what human labor looks like. As a first step, Tesla plans to deploy 1,000 Optimus robots inside the cleanrooms of its new Terafab chip facility by 2027, with the goal of cutting human manufacturing errors by 90%. PRODUCTION TIMELINES & PRICING Musk has outlined a clear ramp: limited production is targeted for 2025, starting with more than 1,000 units for internal use across Tesla factories. External sales to other companies could begin in 2026. By 2027, Tesla aims to start high-volume production at Gigafactory Texas with a target of 10 million robots per year. Ultimately, Musk wants to bring the consumer price down to roughly $20,000–$30,000. "GEN 3" DEXTERITY UPGRADES The robot's hardware is advancing quickly. Tesla recently introduced the Gen 3 hands, which feature 22 degrees of freedom and 50 total actuators — roughly doubling the dexterity of earlier versions. These new hands include tactile fingertip sensors with force-torque feedback, enabling Optimus to handle delicate items such as eggs or glass vials without damaging them. THE "DIGITAL OPTIMUS" BRAIN To give the robot both physical skill and real intelligence, Tesla and xAI are developing an architecture called "Digital Optimus" (sometimes referred to internally as Macrohard). It uses a dual-process system: Tesla's vision-based driving AI serves as "System 1," handling fast, instinctive physical actions and balance. xAI's Grok model acts as "System 2," managing high-level reasoning, task planning, and natural conversation. NEXT-GENERATION SILICON Running powerful AI models on a mobile robot requires extreme efficiency. Optimus will be powered by Tesla's upcoming AI5 and AI6 edge-inference chips (produced at the Terafab facility), delivering 40 to 50 times the compute performance of today's chips while keeping power consumption low enough for all-day operation. LUNAR COLONIZATION AND SPACE OPERATIONS While earlier plans called for sending Optimus robots to Mars in 2026, SpaceX has shifted initial focus toward establishing a Moon colony first. Under Musk's "Kardashev Blueprint," large fleets of Optimus robots — referred to as "Optimi" — will be deployed to the lunar surface. Working autonomously around the clock, they will construct habitats, manufacture space-based AI satellites, and build a massive electromagnetic mass driver to launch the "Starmind" AI constellation into deep space.
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"Optimus will be the biggest product ever." - Elon Musk AND THIS IS HOW HE'S GOING TO DO IT.... REDEFINING LABOR ON EARTH The core mission of Optimus is to take over dangerous, repetitive, and monotonous work, making advanced automation accessible to everyone and fundamentally changing what human labor looks like. As a first step, Tesla plans to deploy 1,000 Optimus robots inside the cleanrooms of its new Terafab chip facility by 2027, with the goal of cutting human manufacturing errors by 90%. PRODUCTION TIMELINES & PRICING Musk has outlined a clear ramp: limited production is targeted for 2025, starting with more than 1,000 units for internal use across Tesla factories. External sales to other companies could begin in 2026. By 2027, Tesla aims to start high-volume production at Gigafactory Texas with a target of 10 million robots per year. Ultimately, Musk wants to bring the consumer price down to roughly $20,000–$30,000. "GEN 3" DEXTERITY UPGRADES The robot's hardware is advancing quickly. Tesla recently introduced the Gen 3 hands, which feature 22 degrees of freedom and 50 total actuators — roughly doubling the dexterity of earlier versions. These new hands include tactile fingertip sensors with force-torque feedback, enabling Optimus to handle delicate items such as eggs or glass vials without damaging them. THE "DIGITAL OPTIMUS" BRAIN To give the robot both physical skill and real intelligence, Tesla and xAI are developing an architecture called "Digital Optimus" (sometimes referred to internally as Macrohard). It uses a dual-process system: Tesla's vision-based driving AI serves as "System 1," handling fast, instinctive physical actions and balance. xAI's Grok model acts as "System 2," managing high-level reasoning, task planning, and natural conversation. NEXT-GENERATION SILICON Running powerful AI models on a mobile robot requires extreme efficiency. Optimus will be powered by Tesla's upcoming AI5 and AI6 edge-inference chips (produced at the Terafab facility), delivering 40 to 50 times the compute performance of today's chips while keeping power consumption low enough for all-day operation. LUNAR COLONIZATION AND SPACE OPERATIONS While earlier plans called for sending Optimus robots to Mars in 2026, SpaceX has shifted initial focus toward establishing a Moon colony first. Under Musk's "Kardashev Blueprint," large fleets of Optimus robots — referred to as "Optimi" — will be deployed to the lunar surface. Working autonomously around the clock, they will construct habitats, manufacture space-based AI satellites, and build a massive electromagnetic mass driver to launch the "Starmind" AI constellation into deep space.

Lacey

66,117 просмотров • 13 дней назад

🇺🇸 ELON'S INTERSTELLAR PLAN: TESLA + SPACEX + xAI TO BUILD SUN-POWERED AI IN SPACE Elon is not just building companies anymore. He's building the actual infrastructure of the post-human era: Giant solar arrays + supercomputers orbiting the sun, powered by Tesla batteries/solar, launched by SpaceX, running xAI models. “I think that there's increasingly a convergence between SpaceX and Tesla and xAI in that if the future is solar-powered AI satellites. Which it pretty much needs to be in order to harness a non-trivial amount of the energy of the sun, you have to move to solar-powered AI satellites in deep space, which is a confluence of Tesla expertise and SpaceX expertise, and xAI on the AI front. It does feel like over time there's somewhat of a convergence there.” Then he flexed hard on Tesla: “We're making great progress with autonomous driving… You can literally download the Tesla app. Definitely try it out… We've made a lot of progress with electric vehicles, with battery packs and solar, and very much so with self-driving. Basically, real-world AI. Tesla is the world leader in real-world AI, I would say.” The 3 companies were never separate; they were always pieces of the same insane endgame. And Tesla's real-world AI (robots, cars, Optimus) is already the undisputed king. The convergence is happening right in front of us. Source: Nikhil Kamath, Elon Musk
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🇺🇸 ELON'S INTERSTELLAR PLAN: TESLA + SPACEX + xAI TO BUILD SUN-POWERED AI IN SPACE Elon is not just building companies anymore. He's building the actual infrastructure of the post-human era: Giant solar arrays + supercomputers orbiting the sun, powered by Tesla batteries/solar, launched by SpaceX, running xAI models. “I think that there's increasingly a convergence between SpaceX and Tesla and xAI in that if the future is solar-powered AI satellites. Which it pretty much needs to be in order to harness a non-trivial amount of the energy of the sun, you have to move to solar-powered AI satellites in deep space, which is a confluence of Tesla expertise and SpaceX expertise, and xAI on the AI front. It does feel like over time there's somewhat of a convergence there.” Then he flexed hard on Tesla: “We're making great progress with autonomous driving… You can literally download the Tesla app. Definitely try it out… We've made a lot of progress with electric vehicles, with battery packs and solar, and very much so with self-driving. Basically, real-world AI. Tesla is the world leader in real-world AI, I would say.” The 3 companies were never separate; they were always pieces of the same insane endgame. And Tesla's real-world AI (robots, cars, Optimus) is already the undisputed king. The convergence is happening right in front of us. Source: Nikhil Kamath, Elon Musk

Mario Nawfal

30,827 просмотров • 7 месяцев назад