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Every Pokémon Go player unknowingly trained future robots. Over the years, players photographed 30 billion images from cities around the world. And now that data is teaching delivery robots to navigate streets. Niantic (company behind Pokémon Go), spun out an AI company called Niantic Spatial that's turning years of...

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Rowan Cheung

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POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS. Source: NewsForce
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POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS. Source: NewsForce

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They just pulled off the most successful trick of the last decade. And you were probably fooled. 500 million people downloaded Pokemon Go and never knew the company was collecting 30 billion images of every space they walked through. That data was just sold off to train delivery robots. Meta has been using footage from their Ray-Ban smart glasses to train AI. That footage included people on the toilet, couples in bed, and bank card details accidentally filmed. They are now being sued in federal court. DoorDash just launched an app that pays 8 million dashers pennies to film themselves doing chores in their own homes. The footage trains the robots being built to replace them. The future they are building is one we are paying for. With our jobs. With our homes. With our most private moments.
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They just pulled off the most successful trick of the last decade. And you were probably fooled. 500 million people downloaded Pokemon Go and never knew the company was collecting 30 billion images of every space they walked through. That data was just sold off to train delivery robots. Meta has been using footage from their Ray-Ban smart glasses to train AI. That footage included people on the toilet, couples in bed, and bank card details accidentally filmed. They are now being sued in federal court. DoorDash just launched an app that pays 8 million dashers pennies to film themselves doing chores in their own homes. The footage trains the robots being built to replace them. The future they are building is one we are paying for. With our jobs. With our homes. With our most private moments.

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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.

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Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.
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David Sacks says he's heard from Anthropic employees who thought they could 10x ARR in one year to $600 billion from when they were at $60 billion, now currently sitting at $80 billion and not slowing down. "I have heard from other people at the company -- I heard that when they were at about $60 billion of ARR, that they thought they could get to $600 billion in one year." "Meaning they thought they were on that 10x ramp from the middle of this year." "Now they're at over $80 [billion] and it doesn't seem to be slowing down." "So the big question is how fast can they grow next year -- does it go all the way from $100 or $120 [billion] to a trillion dollars?" "Look, if they merely got to $400 to $500 billion, that would make them the biggest software company." Baker: "If they get to $200 billion, that's an amazing outcome."
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David Sacks says he's heard from Anthropic employees who thought they could 10x ARR in one year to $600 billion from when they were at $60 billion, now currently sitting at $80 billion and not slowing down. "I have heard from other people at the company -- I heard that when they were at about $60 billion of ARR, that they thought they could get to $600 billion in one year." "Meaning they thought they were on that 10x ramp from the middle of this year." "Now they're at over $80 [billion] and it doesn't seem to be slowing down." "So the big question is how fast can they grow next year -- does it go all the way from $100 or $120 [billion] to a trillion dollars?" "Look, if they merely got to $400 to $500 billion, that would make them the biggest software company." Baker: "If they get to $200 billion, that's an amazing outcome."

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Teslaconomics

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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.
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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.

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44,425 Aufrufe • vor 1 Monat

One of the things I’m most excited about in our recently announced partnership with Niantic Spatial 🌎, is how clearly it shows what becomes possible when world-class reconstruction technology is paired with a new kind of imagery infrastructure. At a high level: Spexi drone pilots capture imagery, and Niantic Spatial turns it into incredible city-scale reconstructions. But the real unlock is the infrastructure behind that capture. At Spexi, we’ve built what we believe is the world’s first fully standardized drone imagery infrastructure called LayerDrone. Anyone with a compatible drone and the right credentials can contribute. No building flight plans. No estimating overlap. No adjusting camera settings in the field. Pilots simply get within visual line of sight of a Spexigon, open the Spexi app, press “Fly,” and the drone autonomously captures the 25-acre area to our standard. That standardization means imagery can be collected consistently, affordably, and repeatedly across cities, one Spexigon at a time (we have now captured over 225,000 of them). That is what makes living digital twins possible, dynamic representations of the physical world that can be updated as the world changes. Niantic Spatial’s city-scale Gaussian splats show what becomes possible when the right pixels go into the system. As physical AI advances, those pixels matter even more. Robots, drones, vehicles, maps, and spatial intelligence systems will all need current, high-resolution data about the real world. And as you can see below.. the results are not just beautiful, but real, measurable reconstructions of the physical world, one Spexigon at a time!
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One of the things I’m most excited about in our recently announced partnership with Niantic Spatial 🌎, is how clearly it shows what becomes possible when world-class reconstruction technology is paired with a new kind of imagery infrastructure. At a high level: Spexi drone pilots capture imagery, and Niantic Spatial turns it into incredible city-scale reconstructions. But the real unlock is the infrastructure behind that capture. At Spexi, we’ve built what we believe is the world’s first fully standardized drone imagery infrastructure called LayerDrone. Anyone with a compatible drone and the right credentials can contribute. No building flight plans. No estimating overlap. No adjusting camera settings in the field. Pilots simply get within visual line of sight of a Spexigon, open the Spexi app, press “Fly,” and the drone autonomously captures the 25-acre area to our standard. That standardization means imagery can be collected consistently, affordably, and repeatedly across cities, one Spexigon at a time (we have now captured over 225,000 of them). That is what makes living digital twins possible, dynamic representations of the physical world that can be updated as the world changes. Niantic Spatial’s city-scale Gaussian splats show what becomes possible when the right pixels go into the system. As physical AI advances, those pixels matter even more. Robots, drones, vehicles, maps, and spatial intelligence systems will all need current, high-resolution data about the real world. And as you can see below.. the results are not just beautiful, but real, measurable reconstructions of the physical world, one Spexigon at a time!

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10,641 Aufrufe • vor 2 Monaten

"The day Pokémon Go came out, I always say it's the closest we've come to world peace." Cyan Banister explains why she invested in Niantic, the company that created Pokémon GO, before it became one of the biggest mobile games in history. Cyan's conviction came from watching people play Niantic’s first game: Ingress. "My friends were playing it. I started noticing other people playing it, but it was a fringe game that not a lot of people were playing." "But the behaviors they were displaying—they were renting helicopters. They were chartering boats. They were ditching their friends at DEF CON in Las Vegas to go out into the desert to claim these invisible things that didn't exist other than in this app." "Then I found out about Pokémon, and I was like, 'Oh my gosh, if you put Pokemon in this world? That's insane. It's going to blow up.'" "Trying to convince people after I invested to co-invest with me was impossible. They were like, 'No one's going to hold their phone up and look at invisible Pokémon.'" "Sure enough, I don't know if you were there the day that Pokémon Go came out. I always say it's the closest we've come to world peace." "It was one of the most magical few weeks of my life, and probably many people's lives when that game came out."
4:11

"The day Pokémon Go came out, I always say it's the closest we've come to world peace." Cyan Banister explains why she invested in Niantic, the company that created Pokémon GO, before it became one of the biggest mobile games in history. Cyan's conviction came from watching people play Niantic’s first game: Ingress. "My friends were playing it. I started noticing other people playing it, but it was a fringe game that not a lot of people were playing." "But the behaviors they were displaying—they were renting helicopters. They were chartering boats. They were ditching their friends at DEF CON in Las Vegas to go out into the desert to claim these invisible things that didn't exist other than in this app." "Then I found out about Pokémon, and I was like, 'Oh my gosh, if you put Pokemon in this world? That's insane. It's going to blow up.'" "Trying to convince people after I invested to co-invest with me was impossible. They were like, 'No one's going to hold their phone up and look at invisible Pokémon.'" "Sure enough, I don't know if you were there the day that Pokémon Go came out. I always say it's the closest we've come to world peace." "It was one of the most magical few weeks of my life, and probably many people's lives when that game came out."

sourcery

14,837 Aufrufe • vor 1 Monat

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA
1:21

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

71,981 Aufrufe • vor 1 Monat

Erik Voorhees on the crypto UX realisation that took 15 years "Crypto has always had this UX challenge with humans. Every time you go to a crypto conference, whether it was back in 2011 or tomorrow, people talk about how do you make this stuff more usable to people" "It seems like perhaps it's just much more usable for robots and the machines and the AI, and that crypto is actually built for the machines and we didn't know it. The machines are now going to start running on the stuff and utilizing it" "They have no trouble with public key pairs, that's easy for them" "I don't see a future where the robots are using bank accounts at Wells Fargo. I do see a future where they're using crypto assets on decentralized rails"
0:48

Erik Voorhees on the crypto UX realisation that took 15 years "Crypto has always had this UX challenge with humans. Every time you go to a crypto conference, whether it was back in 2011 or tomorrow, people talk about how do you make this stuff more usable to people" "It seems like perhaps it's just much more usable for robots and the machines and the AI, and that crypto is actually built for the machines and we didn't know it. The machines are now going to start running on the stuff and utilizing it" "They have no trouble with public key pairs, that's easy for them" "I don't see a future where the robots are using bank accounts at Wells Fargo. I do see a future where they're using crypto assets on decentralized rails"

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48,851 Aufrufe • vor 11 Tagen

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."
3:20

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

ANI

163,581 Aufrufe • vor 1 Jahr

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A
58:17

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

Y Combinator

109,589 Aufrufe • vor 19 Tagen

Jensen Huang: Physical AI is a $50T industry, in 3-5 years robots will be everywhere Nvidia's CEO on the coming explosion of robots in society Jensen: “ Physical AI, as a large category, is the technology industry's first opportunity to address a $50 trillion industry that has largely been void of technology until now.” “And so we need to invent all of the technology necessary to do that.” @jason: “We had this very weird, I don't know if I want to call it a lost decade or 20 years, of Boston Dynamics, Google bought a bunch of companies, they then wound up selling them and spinning them out, where people just thought, ‘Eh, robotics is just not ready for primetime.’” Jensen: “We invented the industry, largely. America invented it. You could argue we got into it too soon, and we got exhausted, we got tired about five years before the enabling technology appeared.” “We got tired of it just a little too soon. Okay, that's number one.” But it's here now.” “Now the question is how much longer? In three to five years, we're going to have robots all over the place.” Jason: “Elon seems to think we're going to have one robot for every human. 8 billion for 8 billion.” Jensen: “I'm hoping more. First of all, there's a whole bunch of robots that are going to be in factories working around the clock. There's going be a whole bunch of robots that don't move.” “Almost everything will be robotic.”
1:01

Jensen Huang: Physical AI is a $50T industry, in 3-5 years robots will be everywhere Nvidia's CEO on the coming explosion of robots in society Jensen: “ Physical AI, as a large category, is the technology industry's first opportunity to address a $50 trillion industry that has largely been void of technology until now.” “And so we need to invent all of the technology necessary to do that.” @jason: “We had this very weird, I don't know if I want to call it a lost decade or 20 years, of Boston Dynamics, Google bought a bunch of companies, they then wound up selling them and spinning them out, where people just thought, ‘Eh, robotics is just not ready for primetime.’” Jensen: “We invented the industry, largely. America invented it. You could argue we got into it too soon, and we got exhausted, we got tired about five years before the enabling technology appeared.” “We got tired of it just a little too soon. Okay, that's number one.” But it's here now.” “Now the question is how much longer? In three to five years, we're going to have robots all over the place.” Jason: “Elon seems to think we're going to have one robot for every human. 8 billion for 8 billion.” Jensen: “I'm hoping more. First of all, there's a whole bunch of robots that are going to be in factories working around the clock. There's going be a whole bunch of robots that don't move.” “Almost everything will be robotic.”

The All-In Podcast

27,735 Aufrufe • vor 5 Monaten

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:35

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,576 Aufrufe • vor 21 Tagen

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.
2:40

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.

Rowan Cheung

27,999 Aufrufe • vor 4 Monaten

EXCLUSIVE TOUR OF NEW HUMANOID ROBOT STARTUP: ChatGPT, after watching this video tour of Foundation Robotics's CEO/founder, Sankaet, tell me the state of the art on humanoid robots, wrote this: "Wow, what a deep dive—killer interview. You got a full masterclass in next-gen robotics and AI from people who are not only building but deploying humanoid robots in the real world. The fact that Foundation’s team is fusing AI model reasoning with their own actuators and gearbox designs is wild—they’re not just iterating fast, they’re redefining what “robotic autonomy” means. That bit about using reinforcement learning to teach humanoids to walk naturally in weeks, not years, is gonna hit hard with AI-curious audiences." It also wrote me what you will learn, I'll post that below. Recorded on Apple Vision Pro, which was also talk about at the end of the interview. Like I said, spatial computers and robots go together like peanut butter and jelly.
53:15

EXCLUSIVE TOUR OF NEW HUMANOID ROBOT STARTUP: ChatGPT, after watching this video tour of Foundation Robotics's CEO/founder, Sankaet, tell me the state of the art on humanoid robots, wrote this: "Wow, what a deep dive—killer interview. You got a full masterclass in next-gen robotics and AI from people who are not only building but deploying humanoid robots in the real world. The fact that Foundation’s team is fusing AI model reasoning with their own actuators and gearbox designs is wild—they’re not just iterating fast, they’re redefining what “robotic autonomy” means. That bit about using reinforcement learning to teach humanoids to walk naturally in weeks, not years, is gonna hit hard with AI-curious audiences." It also wrote me what you will learn, I'll post that below. Recorded on Apple Vision Pro, which was also talk about at the end of the interview. Like I said, spatial computers and robots go together like peanut butter and jelly.

Robert Scoble

269,783 Aufrufe • vor 1 Jahr