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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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38,111 views • 4 months ago •via X (Twitter)

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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
3:24

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

NewsForce

23,674,293 views • 5 months ago

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.
1:13

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.

The White Rabbit

88,321 views • 2 months ago

The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.
1:56

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

GEODNET

146,441 views • 1 month ago

From a small Noida facility six years ago to exporting robots to 25 countries 🌍🤖 Inside Addverb largest mobile robotics factory, 900 engineers are building machines that now power warehouses and industries across India, the US, Europe, Australia and beyond. With capacity to manufacture 100,000 robots a year and a target of 50% India, 50% global revenue, this is a company betting big on “Made in India, for the world.” Shereen Bhan gets an inside look at how a decade-old startup is scaling into a global automation player. #YoungTurksReloaded #Addverb #Robotics #MakeInIndia #AI #Automation #IndianStartups
2:25

From a small Noida facility six years ago to exporting robots to 25 countries 🌍🤖 Inside Addverb largest mobile robotics factory, 900 engineers are building machines that now power warehouses and industries across India, the US, Europe, Australia and beyond. With capacity to manufacture 100,000 robots a year and a target of 50% India, 50% global revenue, this is a company betting big on “Made in India, for the world.” Shereen Bhan gets an inside look at how a decade-old startup is scaling into a global automation player. #YoungTurksReloaded #Addverb #Robotics #MakeInIndia #AI #Automation #IndianStartups

CNBC-TV18

14,035 views • 5 months ago

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.
2:17

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.

Brandon Ong

163,315 views • 4 days ago

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 views • 6 months ago

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

44,368 views • 26 days ago

"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 views • 1 month ago

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!
1:54

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!

Alec Wilson

10,641 views • 2 months ago

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

70,906 views • 24 days ago

#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 views • 1 year ago

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

31,950 views • 1 day ago

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 views • 4 months ago

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 views • 3 months ago

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 views • 1 year ago

Can United States manufacture robots? Matic Robots says "yes." It makes the best floor cleaning robot, that has won many perfect scores from Wired to many others. We love ours. But my trip there to get a tour from AI pioneer Navneet Dalal Navneet Dalal provided some real insights into how hard it is for a hardware company to make hardware in the United States. And how deeply AI is changing consumer electronics products that are going to be in many more homes soon. In this first part (Part II coming tomorrow) we get a look at how long it took for this company to go through prototypes to a shipping product. In the second part, you'll see the scaling hell that it takes to even ship a few thousand robots and the kinds of problems that scaling up a factory brings. Matic is one of my favorite small Silicon Valley companies. It has found what we call "product market fit." I just came back from CES where I saw many of its competitors, and the Matic wins because of not just the product thinking of Mehul and Navneet Dalal but because of their AI leadership. In a way their robot took many lessons from Tesla, from where to put the batteries to its bet on computer vision, which Navneet has been a pioneer in for years, working quietly behind the scenes. It is about to move into a new location that will allow it to grow to meet the demand that now is showing up (the boxes in its lobby show that it's outgrowing its current facilities). In terms of AI, it has aspirations of making a humanoid too, but it is taking a far more measured approach to getting there. By starting on the floor it can not just build world models based on real world data (customers are given a choice whether to allow its data to be used that way. Most customers choose to keep their data on the robot only, for privacy reasons, but if you opt in you can help them improve their models). They are using that data to understand homes. Navneet told me they hit very unusual situations in people's homes already that they couldn't really predict in simulators, like full-wall mirrors that confuse computer vision systems, or pools and water features in people's homes. Having real customers brings a ton of customer feedback about how to further improve the robot, and, as Navneet demonstrates in the second video, forces them to build a manufacturing muscle memory. Getting teams to work together, figuring out how to solve supply chain problems, from Trump's tarriffs, to a new one that showed up over the past couple of weeks. A supplier for its bags (one of the cheaper parts that goes into the robot) changed the glue it used, which caused robots to fail quality tests and the manufacturing line to stop. Reminds me a lot of the hell Elon Musk faced in its Fremont factory when Tesla was first starting to manufacture its Model 3, which almost bankrupted the company. Off the record Mehul and Navneet 🇮🇳 showed me some of the prototypes and plans for its next products that will show up over the next few years. Certainly not as sexy as Tesla, Figure, 1x_tech, and all the Chinese manufacturers are showing off already, but far better thought out for the typical Western home and AI plays a huge role in its future. It is the product that speaks for itself. It's amazing, and is about to get better this year due to AI. It's the first real vision-only robot to be in my home and I bet it won't be the last from this company. Real honor that they invited me over with my Insta360 camera (another company launched in my home, just like Matic was last year). In Part II we go into the factory.
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Can United States manufacture robots? Matic Robots says "yes." It makes the best floor cleaning robot, that has won many perfect scores from Wired to many others. We love ours. But my trip there to get a tour from AI pioneer Navneet Dalal Navneet Dalal provided some real insights into how hard it is for a hardware company to make hardware in the United States. And how deeply AI is changing consumer electronics products that are going to be in many more homes soon. In this first part (Part II coming tomorrow) we get a look at how long it took for this company to go through prototypes to a shipping product. In the second part, you'll see the scaling hell that it takes to even ship a few thousand robots and the kinds of problems that scaling up a factory brings. Matic is one of my favorite small Silicon Valley companies. It has found what we call "product market fit." I just came back from CES where I saw many of its competitors, and the Matic wins because of not just the product thinking of Mehul and Navneet Dalal but because of their AI leadership. In a way their robot took many lessons from Tesla, from where to put the batteries to its bet on computer vision, which Navneet has been a pioneer in for years, working quietly behind the scenes. It is about to move into a new location that will allow it to grow to meet the demand that now is showing up (the boxes in its lobby show that it's outgrowing its current facilities). In terms of AI, it has aspirations of making a humanoid too, but it is taking a far more measured approach to getting there. By starting on the floor it can not just build world models based on real world data (customers are given a choice whether to allow its data to be used that way. Most customers choose to keep their data on the robot only, for privacy reasons, but if you opt in you can help them improve their models). They are using that data to understand homes. Navneet told me they hit very unusual situations in people's homes already that they couldn't really predict in simulators, like full-wall mirrors that confuse computer vision systems, or pools and water features in people's homes. Having real customers brings a ton of customer feedback about how to further improve the robot, and, as Navneet demonstrates in the second video, forces them to build a manufacturing muscle memory. Getting teams to work together, figuring out how to solve supply chain problems, from Trump's tarriffs, to a new one that showed up over the past couple of weeks. A supplier for its bags (one of the cheaper parts that goes into the robot) changed the glue it used, which caused robots to fail quality tests and the manufacturing line to stop. Reminds me a lot of the hell Elon Musk faced in its Fremont factory when Tesla was first starting to manufacture its Model 3, which almost bankrupted the company. Off the record Mehul and Navneet 🇮🇳 showed me some of the prototypes and plans for its next products that will show up over the next few years. Certainly not as sexy as Tesla, Figure, 1x_tech, and all the Chinese manufacturers are showing off already, but far better thought out for the typical Western home and AI plays a huge role in its future. It is the product that speaks for itself. It's amazing, and is about to get better this year due to AI. It's the first real vision-only robot to be in my home and I bet it won't be the last from this company. Real honor that they invited me over with my Insta360 camera (another company launched in my home, just like Matic was last year). In Part II we go into the factory.

Robert Scoble

69,229 views • 6 months ago

A simple idea. Let robots collect the data that current foundation models are missing. A robot that gets better by doing real work in the real world. For two weeks in the Stanford East Asia Library, Scanford scanned shelves, helped librarians, and improved the vision language model it depends on. The idea is very simple: Robots do useful work. They gather the real world data foundation models never see online. They fine tune their own model They go back out stronger A full loop. What they found in deployment: ✅ 2103 shelves scanned with multilingual, faded, occluded book spines ✅ 18.7 hours of librarian time saved ✅ Book ID accuracy jumped from 32.0 percent to 71.8 percent ✅ English OCR improved from 24.8 percent to 46.6 percent ✅ Chinese OCR improved from 30.8 percent to 38.0 percent The most interesting part is the shift. Robots do not only consume foundation models. They create the data these models are missing. A clean robot powered data flywheel. Work. Collect. Fine tune. Repeat. Thanks for sharing, Jenn Grannen! If you want the full write up: 📍Website: Paper: —- Weekly robotics and AI insights. Subscribe free:
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A simple idea. Let robots collect the data that current foundation models are missing. A robot that gets better by doing real work in the real world. For two weeks in the Stanford East Asia Library, Scanford scanned shelves, helped librarians, and improved the vision language model it depends on. The idea is very simple: Robots do useful work. They gather the real world data foundation models never see online. They fine tune their own model They go back out stronger A full loop. What they found in deployment: ✅ 2103 shelves scanned with multilingual, faded, occluded book spines ✅ 18.7 hours of librarian time saved ✅ Book ID accuracy jumped from 32.0 percent to 71.8 percent ✅ English OCR improved from 24.8 percent to 46.6 percent ✅ Chinese OCR improved from 30.8 percent to 38.0 percent The most interesting part is the shift. Robots do not only consume foundation models. They create the data these models are missing. A clean robot powered data flywheel. Work. Collect. Fine tune. Repeat. Thanks for sharing, Jenn Grannen! If you want the full write up: 📍Website: Paper: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

44,660 views • 8 months ago

Demystifying China's Dancing Robots: How Did They Catch Handkerchiefs?🇨🇳🤖 16 humanoid robots from Chinese robotics company, Unitree, took center stage at the annual #SpringFestivalGala. The robots seamlessly coordinated with 16 human dancers to perform a traditional Yangko dance, a vibrant folk art form from northeast China, blending cultural heritage with cutting-edge technology. One of the most captivating moments came when the robots showcased their ability to manipulate handkerchiefs, a signature element of Yangko dance. With precise mechanical arm movements, the robots sent the handkerchiefs twirling and soaring through the air, creating a dazzling visual spectacle that symbolized the perfect fusion of tradition and modernity. To maintain the stable upright standing position is already a challenge for current humanoid robots – consider the shaky steps and tendency to roll off even a small incline of Elon Musk's Optimus. To toss a handkerchief and catch it back in place requires the integration of sensors, algorithm and smart design. "We've designed a very clever mechanism that integrates multiple AI control algorithms. There are two motors at the end of the robotic arm: one maintains a high-speed spinning motion, while the other ensures that the handkerchief can be thrown out and then retracted," Unitree's marketing representative said. The 16 humanoid robots belong to Unitree's H1 series, nicknamed Fuxi. Standing at 1.8 meters tall and weighing 47 kilograms, the robots took the stage at the Spring Festival Gala stage over a year after debuting in August 2023. They also attended the NVIDIA GTC conference in 2024. #ChineseNewYear #DeepSeek (Link:
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Demystifying China's Dancing Robots: How Did They Catch Handkerchiefs?🇨🇳🤖 16 humanoid robots from Chinese robotics company, Unitree, took center stage at the annual #SpringFestivalGala. The robots seamlessly coordinated with 16 human dancers to perform a traditional Yangko dance, a vibrant folk art form from northeast China, blending cultural heritage with cutting-edge technology. One of the most captivating moments came when the robots showcased their ability to manipulate handkerchiefs, a signature element of Yangko dance. With precise mechanical arm movements, the robots sent the handkerchiefs twirling and soaring through the air, creating a dazzling visual spectacle that symbolized the perfect fusion of tradition and modernity. To maintain the stable upright standing position is already a challenge for current humanoid robots – consider the shaky steps and tendency to roll off even a small incline of Elon Musk's Optimus. To toss a handkerchief and catch it back in place requires the integration of sensors, algorithm and smart design. "We've designed a very clever mechanism that integrates multiple AI control algorithms. There are two motors at the end of the robotic arm: one maintains a high-speed spinning motion, while the other ensures that the handkerchief can be thrown out and then retracted," Unitree's marketing representative said. The 16 humanoid robots belong to Unitree's H1 series, nicknamed Fuxi. Standing at 1.8 meters tall and weighing 47 kilograms, the robots took the stage at the Spring Festival Gala stage over a year after debuting in August 2023. They also attended the NVIDIA GTC conference in 2024. #ChineseNewYear #DeepSeek (Link:

Li Jingjing 李菁菁

11,169 views • 1 year ago

BBREAKING: A German robotics startup from Stuttgart just gave robots imagination: Production robotics system where robots evaluate the long-term consequences of their actions before executing them in live industrial environments. Until now, every production robot optimised actions locally; reacting to what it sees right now. The problem? Small errors early in a sequence compound over time. A slightly off pick leads to a jam three steps later. A marginal placement leads to a collision five steps after that. Cortex 2.0 from Sereact introduces decision-grounded world models directly into live operations. The system evaluates alternative action sequences, predicts how risk accumulates, and estimates the likelihood of entering unrecoverable states, before the robot commits. The world model is trained exclusively on real-world execution data. No synthetic simulation. No approximate environment models. Learned from how robots actually fail, recover, and succeed in production. Works across form factors, pick-and-place arms, dual-arm systems, and humanoids. One intelligence layer, any robot body. The Stuttgart-based company Sereact raised a €25M Series A led by Creandum last year, backed by Air Street Capital Capital, Point Nine 🇺🇦 and angels including Nico Rosberg. They already run some of the most productive AI-driven robotic systems in live warehouse environments, with customers including Daimler Truck AG and Bol. ... Stuttgart, not San Francisco. 🇩🇪 Kudos to Ralf Gulde and team! Credit:
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BBREAKING: A German robotics startup from Stuttgart just gave robots imagination: Production robotics system where robots evaluate the long-term consequences of their actions before executing them in live industrial environments. Until now, every production robot optimised actions locally; reacting to what it sees right now. The problem? Small errors early in a sequence compound over time. A slightly off pick leads to a jam three steps later. A marginal placement leads to a collision five steps after that. Cortex 2.0 from Sereact introduces decision-grounded world models directly into live operations. The system evaluates alternative action sequences, predicts how risk accumulates, and estimates the likelihood of entering unrecoverable states, before the robot commits. The world model is trained exclusively on real-world execution data. No synthetic simulation. No approximate environment models. Learned from how robots actually fail, recover, and succeed in production. Works across form factors, pick-and-place arms, dual-arm systems, and humanoids. One intelligence layer, any robot body. The Stuttgart-based company Sereact raised a €25M Series A led by Creandum last year, backed by Air Street Capital Capital, Point Nine 🇺🇦 and angels including Nico Rosberg. They already run some of the most productive AI-driven robotic systems in live warehouse environments, with customers including Daimler Truck AG and Bol. ... Stuttgart, not San Francisco. 🇩🇪 Kudos to Ralf Gulde and team! Credit:

Ilir Aliu

72,419 views • 5 months ago