Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots... show more

Ali Ansari

28,046 subscribers

208,832 просмотров • 6 месяцев назад •via X (Twitter)

Образование Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 34

Фото профиля Rambo

Rambo6 месяцев назад

Who's gunna tell him? $KLED Szn

Фото профиля Fabio Andres Aguilera-Convers

Fabio Andres Aguilera-Convers6 месяцев назад

🤖

Фото профиля ZeusTHEgreat

ZeusTHEgreat6 месяцев назад

Some where in those countless of human data, I made a mark Happy to see big progress from micro1

Фото профиля Arian

Arian6 месяцев назад

Super Bowl quality (the ad and our data)

Фото профиля Jerome Josz

Jerome Josz6 месяцев назад

let's go! great video + lab

Фото профиля Andy Folio

Andy Folio6 месяцев назад

why not use kled

Фото профиля Jimmy Loram

Jimmy Loram6 месяцев назад

The day has finally come where instead of humans doing "the robot" we have robots doing "the human!"

Фото профиля Brandon

Brandon6 месяцев назад

what a video man @danielwarnur 🐐

Фото профиля Julia

Julia6 месяцев назад

Just the beginning!!!

Фото профиля A.L. Trenchman

A.L. Trenchman6 месяцев назад

Blatant copycat of @useKled

Фото профиля Tyler

Tyler6 месяцев назад

and so it begins.... 🤖🫶

Фото профиля Sydney

Sydney6 месяцев назад

The future is here!!

Фото профиля AshutoshShrivastava

AshutoshShrivastava6 месяцев назад

Awesome Ad bro and I will try to submit some videos 😂

Фото профиля Nima Yazdani

Nima Yazdani6 месяцев назад

👀…

Фото профиля Ava

Ava6 месяцев назад

@mon__lim Sooo awesome

Фото профиля Monica L

Monica L6 месяцев назад

incredible!!! 🤖🦾🚀🚀

Фото профиля Crypto Zully🔋(🌐,🌐)

Crypto Zully🔋(🌐,🌐)6 месяцев назад

$kled @useKled

Фото профиля Ryan Eisenman

Ryan Eisenman6 месяцев назад

The future is here!

Фото профиля Dan Westgarth

Dan Westgarth6 месяцев назад

🔥🔥🔥

Фото профиля Samuel Gifted

Samuel Gifted6 месяцев назад

Glad to be part of this achievement.

Фото профиля Heisenberg

Heisenberg6 месяцев назад

Robotics/AI in your house is coming sooner than we think. Scurrry!

Фото профиля Jawsh ooo ah

Jawsh ooo ah6 месяцев назад

Stolen content and the idea from @useKled haha. Directly stealing video content aswell, absolutely shameless behaviour😂😂 $kled coded fr

Фото профиля Hammad Qadeer

Hammad Qadeer6 месяцев назад

Awesome ❤️

Фото профиля DOGO2541

DOGO25416 месяцев назад

The future of robot..here we come

Фото профиля AIbolic

AIbolic6 месяцев назад

The ad vibes are awesome.

Фото профиля Nitin Bisht

Nitin Bisht6 месяцев назад

The internet trained AI to talk. This trains AI to act. That’s the gap every lab is racing to close right now.

Фото профиля mrkelly

mrkelly6 месяцев назад

Data quality usually drops as scale and diversity explode.

Фото профиля RUTH E. ADEYEMI

RUTH E. ADEYEMI6 месяцев назад

Awesome 👏 The future is here

Фото профиля Nakul Bang

Nakul Bang6 месяцев назад

🤯🦾

Фото профиля Porters Reserve

Porters Reserve6 месяцев назад

Finally seen some gardening bet your unique environments dosent include a dense polyculture farm. Reach out to the reserve and see the volume of data you would get with us in the crucible.

Фото профиля I will win

I will win6 месяцев назад

Good job

Фото профиля diva🤭

diva🤭6 месяцев назад

The future of Ai and robot is here 🔥

Фото профиля Aidan Gold

Aidan Gold6 месяцев назад

this is such a vibe

Фото профиля Kwame Walker

Kwame Walker6 месяцев назад

Here we go! ❤️

Похожие видео

Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild 🙌🏕️ Human data collection system that works in diverse environments, without robots 💪🦾 Human + Robot Cotraining pipeline that unlocks generalization 🧵👇

Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild 🙌🏕️ Human data collection system that works in diverse environments, without robots 💪🦾 Human + Robot Cotraining pipeline that unlocks generalization 🧵👇

Tony Tao

97,762 просмотров • 1 год назад

Cortex AI (CortexAI) produces the world's most diverse egocentric and robot dataset, captured in real workplace environments. Leading research labs use Cortex AI to collect data and deploy robotics foundation models in real-world settings.

Cortex AI (CortexAI) produces the world's most diverse egocentric and robot dataset, captured in real workplace environments. Leading research labs use Cortex AI to collect data and deploy robotics foundation models in real-world settings.

Y Combinator

16,336 просмотров • 10 месяцев назад

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence, frontier labs can train on RL environments across every subject matter all in one place.

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence, frontier labs can train on RL environments across every subject matter all in one place.

Ali Ansari

333,176 просмотров • 10 месяцев назад

Physical AI is quickly becoming one of the biggest stories in tech. As robots move beyond controlled environments and into the real world, the need for high-quality real-world data grows with them. jacob on why that matters ↓

Physical AI is quickly becoming one of the biggest stories in tech. As robots move beyond controlled environments and into the real world, the need for high-quality real-world data grows with them. jacob on why that matters ↓

The DATA Foundation

16,877 просмотров • 1 месяц назад

Axis is officially LIVE on Base. 🔵 Axis is scaling Physical AI for the real world, contributed by everyone. You can control robots in a virtual world, generate training data at scale, and help build the brain behind tomorrow's robots. All from browser. No hardware needed. Start building robotics intelligence today:

Axis is officially LIVE on Base. 🔵 Axis is scaling Physical AI for the real world, contributed by everyone. You can control robots in a virtual world, generate training data at scale, and help build the brain behind tomorrow's robots. All from browser. No hardware needed. Start building robotics intelligence today:

Axis Robotics

436,031 просмотров • 6 месяцев назад

Today we’re launching Coco Robotics’ new Physical AI Lab led by Bolei Zhou, one of the world’s most cited AI researchers, and our new Chief AI Scientist. Our fleet faces unpredictable environments daily. That real world data is the key to advancing physical AI.

Today we’re launching Coco Robotics’ new Physical AI Lab led by Bolei Zhou, one of the world’s most cited AI researchers, and our new Chief AI Scientist. Our fleet faces unpredictable environments daily. That real world data is the key to advancing physical AI.

Zach Rash

142,768 просмотров • 11 месяцев назад

What’s next in robotics starts with physical AI. 🤖 By combining decades of machine data with AI, simulation, and digital twins, leaders like AGIBOT, FRANKA ROBOTICS, CaterpillarInc, and PlusAI are scaling intelligent machines across industries and redefining what robots can do in the physical world. Watch the full video 📹

What’s next in robotics starts with physical AI. 🤖 By combining decades of machine data with AI, simulation, and digital twins, leaders like AGIBOT, FRANKA ROBOTICS, CaterpillarInc, and PlusAI are scaling intelligent machines across industries and redefining what robots can do in the physical world. Watch the full video 📹

NVIDIA Robotics

39,481 просмотров • 7 месяцев назад

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

a16z

77,065 просмотров • 1 месяц назад

For roboticists, one challenge towers above the others: there isn’t enough data. To accelerate the deployment of intelligent robots in the real world, MIT CSAIL’s "LucidSim" uses genAI & physics engines to create diverse & realistic virtual training grounds for robots. W/o any real-world data, their robot achieved expert-level performance in difficult tasks:

For roboticists, one challenge towers above the others: there isn’t enough data. To accelerate the deployment of intelligent robots in the real world, MIT CSAIL’s "LucidSim" uses genAI & physics engines to create diverse & realistic virtual training grounds for robots. W/o any real-world data, their robot achieved expert-level performance in difficult tasks:

MIT CSAIL

82,935 просмотров • 1 год назад

AI is only as good as its data. Everyone talks about building smarter AI models, but fewer people ask where the training data comes from. That’s what makes Humyn Labs interesting. They’re focused on collecting high-quality, real-world human data across different environments, behaviors, and sensory inputs. Because if AI is going to understand the physical world, it first needs to learn from the people who already live and work in it. I shared my thoughts in this short video. Let me know what stood out to you. 👇🎥 Humyn Labs KGeN Community #KGeN

AI is only as good as its data. Everyone talks about building smarter AI models, but fewer people ask where the training data comes from. That’s what makes Humyn Labs interesting. They’re focused on collecting high-quality, real-world human data across different environments, behaviors, and sensory inputs. Because if AI is going to understand the physical world, it first needs to learn from the people who already live and work in it. I shared my thoughts in this short video. Let me know what stood out to you. 👇🎥 Humyn Labs KGeN Community #KGeN

Bahdfella™ ❤️‍🔥

21,966 просмотров • 2 месяцев назад

The internet was the training set for intelligence Nobody has built the equivalent for the physical world Previously, Lucas Ngoo co-founded Carousell, scaled it $1B+ Now at CortexAI he's collecting the data robotics labs need to train foundation models Cameras, VR headsets, glasses on factory workers, retail workers, everyday people. Recording real-world manipulation work Not building the robot. Not training the model. Collecting what goes in 2026: scale data 2027+: start rolling robots out Initially 90% teleoperation, 10% autonomy While humans step in, the system keeps learning That's the flywheel toward full autonomy 🎙️ Lucas Ngoo, Co-Founder & CEO, CortexAI on Fondo.com Start (full ep in comments)

The internet was the training set for intelligence Nobody has built the equivalent for the physical world Previously, Lucas Ngoo co-founded Carousell, scaled it $1B+ Now at CortexAI he's collecting the data robotics labs need to train foundation models Cameras, VR headsets, glasses on factory workers, retail workers, everyday people. Recording real-world manipulation work Not building the robot. Not training the model. Collecting what goes in 2026: scale data 2027+: start rolling robots out Initially 90% teleoperation, 10% autonomy While humans step in, the system keeps learning That's the flywheel toward full autonomy 🎙️ Lucas Ngoo, Co-Founder & CEO, CortexAI on Fondo.com Start (full ep in comments)

David J Phillips

110,268 просмотров • 6 месяцев назад

Introducing 𝐃𝐫𝐞𝐚𝐦𝐆𝐞𝐧! We got humanoid robots to perform totally new 𝑣𝑒𝑟𝑏𝑠 in new environments through video world models. We believe video world models will solve the data problem in robotics. Bringing the paradigm of scaling human hours to GPU hours. Quick 🧵

Introducing 𝐃𝐫𝐞𝐚𝐦𝐆𝐞𝐧! We got humanoid robots to perform totally new 𝑣𝑒𝑟𝑏𝑠 in new environments through video world models. We believe video world models will solve the data problem in robotics. Bringing the paradigm of scaling human hours to GPU hours. Quick 🧵

Joel Jang

117,574 просмотров • 1 год назад

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

272,539 просмотров • 19 дней назад

Today Mecka is announcing $60M in funding to become the data and deployment layer for physical AI This raise will allow us to scale our data infrastructure, invest into new verticals, and deploy robots into the real world

Today Mecka is announcing $60M in funding to become the data and deployment layer for physical AI This raise will allow us to scale our data infrastructure, invest into new verticals, and deploy robots into the real world

Josh

334,354 просмотров • 3 месяцев назад

Today we’re introducing flow, micro1’s next-generation data platform for turning human expertise into measurable capability gains. At the core of flow are Realms, micro1’s real-world RL environments where experts establish what strong performance looks like. flow-gen models expand human judgment into new environments, rubrics, variations, and edge cases, while flow-qc models evaluate performance, identify the highest-value failures, and route them back to experts for review. Each cycle produces a stronger training signal and a measurable gain in capability. Those gains compound across frontier models, enterprise agents through Cortex, and robotics, while improving the suite of data generation models recursively. We’re moving beyond producing data to delivering abundant & predictable units of intelligence improvement.

Today we’re introducing flow, micro1’s next-generation data platform for turning human expertise into measurable capability gains. At the core of flow are Realms, micro1’s real-world RL environments where experts establish what strong performance looks like. flow-gen models expand human judgment into new environments, rubrics, variations, and edge cases, while flow-qc models evaluate performance, identify the highest-value failures, and route them back to experts for review. Each cycle produces a stronger training signal and a measurable gain in capability. Those gains compound across frontier models, enterprise agents through Cortex, and robotics, while improving the suite of data generation models recursively. We’re moving beyond producing data to delivering abundant & predictable units of intelligence improvement.

micro1

69,519 просмотров • 5 дней назад

Announcing Axol: a dual-arm robot designed for teams working with physical AI. Made in America. Axol is for builders who believe robots should work, in the real world, not just staged environments, and that the future of physical AI should be open, not closed. #robotics #automation #ai #hardware

Announcing Axol: a dual-arm robot designed for teams working with physical AI. Made in America. Axol is for builders who believe robots should work, in the real world, not just staged environments, and that the future of physical AI should be open, not closed. #robotics #automation #ai #hardware

Almond Robotics

63,842 просмотров • 3 месяцев назад

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 просмотров • 6 месяцев назад

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

Axis Robotics

22,634 просмотров • 1 месяц назад

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

NVIDIA Robotics

10,840 просмотров • 5 месяцев назад