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The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the... field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.show more

Lightwheel

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68,309 Aufrufe • vor 4 Tagen •via X (Twitter)

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100,000 hours of human hands doing real work. LeRobot format, fully annotated and open. EgoSuite-Open100K from Lightwheel X Hugging Face: 15,000+ tasks across 15,000+ real scenes, hand + body pose and wrist cams. Recorded across 7 environment categories and 128 scene types, from kitchens and bedrooms to warehouse floors and assembly lines. It's open for academic research and commercial training, and streams straight from the Hub. Always great to see a dataset at this scale released openly on the Hub. More info:

100,000 hours of human hands doing real work. LeRobot format, fully annotated and open. EgoSuite-Open100K from Lightwheel X Hugging Face: 15,000+ tasks across 15,000+ real scenes, hand + body pose and wrist cams. Recorded across 7 environment categories and 128 scene types, from kitchens and bedrooms to warehouse floors and assembly lines. It's open for academic research and commercial training, and streams straight from the Hub. Always great to see a dataset at this scale released openly on the Hub. More info:

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Imitation learning has a data scarcity problem. Introducing EgoDex from Apple, the largest and most diverse dataset of dexterous human manipulation to date — 829 hours of egocentric video + paired 3D hand poses across 194 tasks. Now on arxiv: (1/4)

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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 →

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42,811 Aufrufe • vor 14 Tagen

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

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Zhi (Leo) Wang

110,230 Aufrufe • vor 3 Monaten

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

RoboHub🤖

43,752 Aufrufe • vor 5 Monaten

The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The Humanoid Hub

44,441 Aufrufe • vor 6 Monaten

Most AI is still learning from the same recycled internet data and that is the real bottleneck no one likes to talk about. The internet is running out of high quality human signal, what remains now is noisy, repetitive, and heavily biased toward the same regions and behaviors. The truth is, physical AI and next generation LLMs need something better like data that comes from real people, real environments, real languages, and real actions. KGeN Foundation 🟩 as a verified human network is changing the game, as it spans more than 60 countries to collects multimodal data including voice, first person video, preferences, and everyday behaviors from actual humans rather than bots or synthetic sources, resulting to a better data from real people for better AI. 𝗘𝘃𝗲𝗿𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝘀 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮. I'm sure you wouldn't want to be left behind, so to be part of this great movement, Go to: to start contributing today.

Most AI is still learning from the same recycled internet data and that is the real bottleneck no one likes to talk about. The internet is running out of high quality human signal, what remains now is noisy, repetitive, and heavily biased toward the same regions and behaviors. The truth is, physical AI and next generation LLMs need something better like data that comes from real people, real environments, real languages, and real actions. KGeN Foundation 🟩 as a verified human network is changing the game, as it spans more than 60 countries to collects multimodal data including voice, first person video, preferences, and everyday behaviors from actual humans rather than bots or synthetic sources, resulting to a better data from real people for better AI. 𝗘𝘃𝗲𝗿𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝘀 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮. I'm sure you wouldn't want to be left behind, so to be part of this great movement, Go to: to start contributing today.

𝐓𝐡𝐞𝐎𝐧𝐥𝐲𝟏𝐇𝐞𝐫𝐨👑

11,150 Aufrufe • vor 3 Tagen

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 Aufrufe • vor 2 Monaten

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

Rerun

29,684 Aufrufe • vor 6 Monaten