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HUMANOID ROBOTS ARE AMAZING. BUT WHERE IS THE ROI? Watching a humanoid robot work at BMW is genuinely impressive. As an engineer, I love it. But I also can’t ignore one question. For this task, why not use a 6-axis robot on a linear axis? It would likely be:...

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11,496 次观看 • 2 个月前 •via X (Twitter)

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Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.
2:07

Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Shruti

59,306 次观看 • 3 个月前

Sam Altman says he’s excited about a future where signing up for ChatGPT’s highest tier gets you a free humanoid robot. ⦿ The mechanical engineering and AI for humanoids are quite hard but feel within grasp. ⦿ Making a billion humanoid robots will take a while; maybe the first million humanoid robots, built the old-fashioned way, will automate the entire robotics supply chain to enable faster scale. ⦿ The demand for humanoids will be far more than what we can handle with current supply chains. ⦿ Things we've tried to bring manufacturing back to the US have failed. AI & robotics is a potential solution.
2:07

Sam Altman says he’s excited about a future where signing up for ChatGPT’s highest tier gets you a free humanoid robot. ⦿ The mechanical engineering and AI for humanoids are quite hard but feel within grasp. ⦿ Making a billion humanoid robots will take a while; maybe the first million humanoid robots, built the old-fashioned way, will automate the entire robotics supply chain to enable faster scale. ⦿ The demand for humanoids will be far more than what we can handle with current supply chains. ⦿ Things we've tried to bring manufacturing back to the US have failed. AI & robotics is a potential solution.

The Humanoid Hub

82,099 次观看 • 1 年前

Yann LeCun says today's humanoid robot demos look impressive, but the robots are very stupid in the real world "the missing piece is not hardware; it is AI that can't reason, plan, or adapt like humans" Robot companies are betting AI will make them smart enough to sell at scale within 3-5 years It's a big bet
1:19

Yann LeCun says today's humanoid robot demos look impressive, but the robots are very stupid in the real world "the missing piece is not hardware; it is AI that can't reason, plan, or adapt like humans" Robot companies are betting AI will make them smart enough to sell at scale within 3-5 years It's a big bet

Haider.

37,274 次观看 • 6 个月前

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.
1:10

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

_Victorad

19,657 次观看 • 1 个月前

🚨🚨🚨Humanoid Robots Just Stepped Into the Ring** This is not really a fight. Not yet. The robots are slow, awkward, unstable, and clearly still learning how to move like athletes. But that is exactly what makes the clip interesting. A boxing ring is one of the hardest places to fake progress. Balance matters. Reaction time matters. Footwork matters. One bad movement, and the machine is on the floor. Today, it looks funny. But every fall is data. Every awkward punch is training. And every public demo like this brings humanoid robots one step closer to moving through the world with real physical confidence. The fight is not between two robots. It is between robotics and gravity.
0:25

🚨🚨🚨Humanoid Robots Just Stepped Into the Ring** This is not really a fight. Not yet. The robots are slow, awkward, unstable, and clearly still learning how to move like athletes. But that is exactly what makes the clip interesting. A boxing ring is one of the hardest places to fake progress. Balance matters. Reaction time matters. Footwork matters. One bad movement, and the machine is on the floor. Today, it looks funny. But every fall is data. Every awkward punch is training. And every public demo like this brings humanoid robots one step closer to moving through the world with real physical confidence. The fight is not between two robots. It is between robotics and gravity.

velora

15,097 次观看 • 1 个月前

11 years of humanoid robotics in one video. 🤖 In 2015, humanoid robots were still struggling with basic tasks like walking, climbing stairs and recovering from falls during the DARPA Robotics Challenge. Fast-forward to 2026, and humanoids are running, jumping, boxing, playing football, handling industrial tasks and operating in real-world environments. The World Humanoid Robot Games featured 2,056 robots across 51 events, including practical tasks in factories, hotels and homes. The progress is obvious. But the real question is not how impressive they look in a demo. What will humanoid robots be capable of in 2036? 👀 🎥 Media: Defense Advanced Research Projects Agency (DARPA) / Unitree Robotics ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technologies shown. Credit belongs to the original creators. #HumanoidRobots #Robotics #PhysicalAI #AI #DARPA #RobotEvolution #FutureOfRobotics #EmbodiedAI #Automation #RobotTechnology #CTORoboticsMedia
0:39

11 years of humanoid robotics in one video. 🤖 In 2015, humanoid robots were still struggling with basic tasks like walking, climbing stairs and recovering from falls during the DARPA Robotics Challenge. Fast-forward to 2026, and humanoids are running, jumping, boxing, playing football, handling industrial tasks and operating in real-world environments. The World Humanoid Robot Games featured 2,056 robots across 51 events, including practical tasks in factories, hotels and homes. The progress is obvious. But the real question is not how impressive they look in a demo. What will humanoid robots be capable of in 2036? 👀 🎥 Media: Defense Advanced Research Projects Agency (DARPA) / Unitree Robotics ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technologies shown. Credit belongs to the original creators. #HumanoidRobots #Robotics #PhysicalAI #AI #DARPA #RobotEvolution #FutureOfRobotics #EmbodiedAI #Automation #RobotTechnology #CTORoboticsMedia

CTO ROBOTICS Media

326,505 次观看 • 13 天前

Elon Musk: “When you make the robots, the robots will start manufacturing the robots, so you get a recursive effect It starts off very slowly, but then it grows at an explosive rate” “10 years from now, I would say there are well over a billion humanoid robots" "And those robots will be at least five times the output of a human And this is a conservative estimate" Meaning: • 1+ billion humanoid robots •~5× human productivity per robot • More combined output than all humans on Earth
1:02

Elon Musk: “When you make the robots, the robots will start manufacturing the robots, so you get a recursive effect It starts off very slowly, but then it grows at an explosive rate” “10 years from now, I would say there are well over a billion humanoid robots" "And those robots will be at least five times the output of a human And this is a conservative estimate" Meaning: • 1+ billion humanoid robots •~5× human productivity per robot • More combined output than all humans on Earth

X Freeze

82,212 次观看 • 23 天前

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,835 次观看 • 9 个月前

Humanoid robots are taking over the largest robotics and AI show in America. Grady Trimble got an inside look at the robot family stealing the show and how they are being used in the real world. "AI is enabling robots to do all kinds of work, stuff like this, and hospitals, in factories, in warehouses... It's not just entertainment," President of the the Association for Advancing Automation Jeff Burnstein says.
1:58

Humanoid robots are taking over the largest robotics and AI show in America. Grady Trimble got an inside look at the robot family stealing the show and how they are being used in the real world. "AI is enabling robots to do all kinds of work, stuff like this, and hospitals, in factories, in warehouses... It's not just entertainment," President of the the Association for Advancing Automation Jeff Burnstein says.

FOX Business

15,437 次观看 • 3 个月前

⚠️ ONE HUMANOID ROBOT IS IMPRESSIVE. WATCHING AN ENTIRE FLEET MOVE AS ONE IS A DIFFERENT LEVEL. Dozens of humanoid robots are performing the same sequence simultaneously, matching their timing across the entire formation. Every step, arm movement and change of position has to stay coordinated while each machine independently maintains its balance. One robot can hide a mistake - a whole formation can't. The choreography is almost beside the point. What you're really seeing is the ability to reproduce complex physical movements consistently across a large number of machines, without the formation falling apart. That kind of repeatability becomes far more important once robots move from controlled demonstrations into real industrial environments. And that's where the real breakthrough could be. The future of humanoid robotics won't be measured only by what one machine can do, but by how reliably an entire fleet can do it. One capable robot is a technological achievement; coordinating many of them turns that capability into a system. One robot proves the technology. A fleet shows what happens when it scales.
1:44

⚠️ ONE HUMANOID ROBOT IS IMPRESSIVE. WATCHING AN ENTIRE FLEET MOVE AS ONE IS A DIFFERENT LEVEL. Dozens of humanoid robots are performing the same sequence simultaneously, matching their timing across the entire formation. Every step, arm movement and change of position has to stay coordinated while each machine independently maintains its balance. One robot can hide a mistake - a whole formation can't. The choreography is almost beside the point. What you're really seeing is the ability to reproduce complex physical movements consistently across a large number of machines, without the formation falling apart. That kind of repeatability becomes far more important once robots move from controlled demonstrations into real industrial environments. And that's where the real breakthrough could be. The future of humanoid robotics won't be measured only by what one machine can do, but by how reliably an entire fleet can do it. One capable robot is a technological achievement; coordinating many of them turns that capability into a system. One robot proves the technology. A fleet shows what happens when it scales.

Future Memo

16,337 次观看 • 1 个月前

ELON: TESLA IS THE WORLD’S BIGGEST ROBOT MAKER AND AI SAFETY LEADER "Tesla is arguably the world's biggest robot maker, because we have basically semi-autonomous cars that will ultimately be fully autonomous. And we are building a humanoid robot that will be basically like the car, but with legs. I kind of held open doing that for a while because I certainly don't want to hasten the AI apocalypse. But clearly, when you look at Boston Dynamics and these humanoid robots, they're going to happen with or without Tesla. So with Tesla, I've got a lot more ability to ensure robotics safety and AI, and I'll try my best to do that." Source: ELON CLIPS
0:42

ELON: TESLA IS THE WORLD’S BIGGEST ROBOT MAKER AND AI SAFETY LEADER "Tesla is arguably the world's biggest robot maker, because we have basically semi-autonomous cars that will ultimately be fully autonomous. And we are building a humanoid robot that will be basically like the car, but with legs. I kind of held open doing that for a while because I certainly don't want to hasten the AI apocalypse. But clearly, when you look at Boston Dynamics and these humanoid robots, they're going to happen with or without Tesla. So with Tesla, I've got a lot more ability to ensure robotics safety and AI, and I'll try my best to do that." Source: ELON CLIPS

Mario Nawfal

62,961 次观看 • 10 个月前

Training humanoid robots on teleoperation alone won’t scale, says Rhoda AI CEO Jagdeep Singh. Unlike self-driving cars - which basically have four actuators (left, right, speed up, and slow down) and operate in a single environment (the road) - humanoid robots are completely different: "You're dealing with the full dexterity of a human hand - 20 degrees of freedom per hand. Every object is different. Every type of task is different." The problem isn't just the quantity of data - the bigger issue is diversity in the data, and it's why many humanoid robot demos will struggle to adapt to the real world: "If all the data you have is data that you've intentionally collected, then you almost by definition haven't seen the corner cases. You haven't seen all those edge scenarios that cause failure." Jagdeep says teleoperation is useful for fine-tuning robot behavior, but for pretraining, it's completely inadequate.
2:46

Training humanoid robots on teleoperation alone won’t scale, says Rhoda AI CEO Jagdeep Singh. Unlike self-driving cars - which basically have four actuators (left, right, speed up, and slow down) and operate in a single environment (the road) - humanoid robots are completely different: "You're dealing with the full dexterity of a human hand - 20 degrees of freedom per hand. Every object is different. Every type of task is different." The problem isn't just the quantity of data - the bigger issue is diversity in the data, and it's why many humanoid robot demos will struggle to adapt to the real world: "If all the data you have is data that you've intentionally collected, then you almost by definition haven't seen the corner cases. You haven't seen all those edge scenarios that cause failure." Jagdeep says teleoperation is useful for fine-tuning robot behavior, but for pretraining, it's completely inadequate.

TBPN

19,218 次观看 • 6 个月前

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:
0:29

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 次观看 • 10 个月前

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:56

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,898 次观看 • 7 个月前

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.
1:01

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

44,534 次观看 • 2 个月前