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The Physical AI data space is exploding, but most vendors still share samples through Google Drive or Hugging Face links that are slow and painful to evaluate. We built the Human Archive Catalog so researchers can quickly browse and filter our off-the-shelf datasets by environment, hardware configuration, modality, annotations,...

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Raj Patel

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12,484 Aufrufe • vor 3 Monaten •via X (Twitter)

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A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

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🚨: HARDWARE FOUNDERS this one is for you! If you’re building: > machines - from CNCs to circuits, from ships (space or sea - doesn't matter) to satellites > robots - arms, quadrupeds, humanoids, semi-humanoids, exoskeletons > drones or UAVs or things that fly > wearables rings or pendants or smart glasses or .... basically anything in hardware, manufacturing, industrial automation, or embodied AI ... we’ve built a Home for you! Whether you’re in Tokyo or Tennessee, Melbourne or Madrid, Singapore or San Francisco, Beijing or Berlin, Shenzhen or Stockholm, Bangalore or Boston, or Lima or London — if you’re building the physical future, you belong here⚒️ MOSAIX is a global network for hardware x AI founders — the first of its kind — designed to help you solve, scale, and sustain your vision. This is born out of years of working with hardware founders across the world and seeing how hard it is to build in hardware alone. Founders like you have a unique journey. You deal with machines, with grease, with soldering irons and sensors, with components and gears. Your challenges don’t look like those in the software world, and that’s exactly why MOSAIX exists. Our team spans China, India, Europe, and the U.S. and so does our purpose. We’re independent. We’re not backed by anyone. We don’t carry anyone’s agenda. Just a shared belief that hardware founders deserve a network of their own — a safe space, and a safety net. MOSAIX is invite-only. For now, joining is free. Welcome to the Age of Hardware. Welcome to the MOSAIX Network. More details in the tweet below.
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🚨: HARDWARE FOUNDERS this one is for you! If you’re building: > machines - from CNCs to circuits, from ships (space or sea - doesn't matter) to satellites > robots - arms, quadrupeds, humanoids, semi-humanoids, exoskeletons > drones or UAVs or things that fly > wearables rings or pendants or smart glasses or .... basically anything in hardware, manufacturing, industrial automation, or embodied AI ... we’ve built a Home for you! Whether you’re in Tokyo or Tennessee, Melbourne or Madrid, Singapore or San Francisco, Beijing or Berlin, Shenzhen or Stockholm, Bangalore or Boston, or Lima or London — if you’re building the physical future, you belong here⚒️ MOSAIX is a global network for hardware x AI founders — the first of its kind — designed to help you solve, scale, and sustain your vision. This is born out of years of working with hardware founders across the world and seeing how hard it is to build in hardware alone. Founders like you have a unique journey. You deal with machines, with grease, with soldering irons and sensors, with components and gears. Your challenges don’t look like those in the software world, and that’s exactly why MOSAIX exists. Our team spans China, India, Europe, and the U.S. and so does our purpose. We’re independent. We’re not backed by anyone. We don’t carry anyone’s agenda. Just a shared belief that hardware founders deserve a network of their own — a safe space, and a safety net. MOSAIX is invite-only. For now, joining is free. Welcome to the Age of Hardware. Welcome to the MOSAIX Network. More details in the tweet below.

AML

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Most capable generalist robotics models today are closed or at best, open weights. But robotics won’t reach its ChatGPT moment without real openness. That GPT moment was built on years of open tools and datasets such as Python, PyTorch, ImageNet and more, that let researchers inspect, reproduce, and build. Today, we’re introducing MolmoAct 2: a fully open-source action reasoning model for real-world robotics. We rethought and reshaped everything! 🧵👇
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Most capable generalist robotics models today are closed or at best, open weights. But robotics won’t reach its ChatGPT moment without real openness. That GPT moment was built on years of open tools and datasets such as Python, PyTorch, ImageNet and more, that let researchers inspect, reproduce, and build. Today, we’re introducing MolmoAct 2: a fully open-source action reasoning model for real-world robotics. We rethought and reshaped everything! 🧵👇

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Project Suncatcher is our moonshot exploring whether we can one day host machine learning infrastructure in space 🚀 To do so, we need to know whether our AI hardware can operate in orbit. Can our TPUs handle the physical stress of spaceflight and the radiation and thermal extremes of space? These are the questions Google researchers have been exploring for the past few years. Last week, we launched our first test satellite carrying four TPUs into orbit — putting us one step closer to getting answers.

Google

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Introducing Trace. Trace is our flagship, public audit and search platform where every asset permanently registered on DATA Network can be accessed. AI Labs can search every human contributed photo or voice sample and drill into any individual record to see the full audit trail. Trace is where labs and regulators can filter by dataset, app, data type, modality, and time to verify data provenance so AI models can be trained with confidence.

The DATA Foundation

77,532 Aufrufe • vor 4 Monaten

This is the part of AI that doesn’t get talked about enough. LLMs had the entire internet to learn from. Robotics doesn’t have that luxury. A robot can’t learn what it feels like to iron clothes while a toddler is running around, restock a shelf in a crowded store, or assemble something on a messy factory floor just by reading about it. It has to experience the physical world. And that’s why what Humyn Labs is building is so important. They’re turning real human experiences into model-ready data across sound, sight, motion and touch, so Physical AI can learn from the world as it actually exists, not some perfectly controlled laboratory. The next breakthrough in robotics may not come from making the model bigger. It may come from giving the model better experiences to learn from. That’s the human layer Physical AI has been missing. 🟧
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This is the part of AI that doesn’t get talked about enough. LLMs had the entire internet to learn from. Robotics doesn’t have that luxury. A robot can’t learn what it feels like to iron clothes while a toddler is running around, restock a shelf in a crowded store, or assemble something on a messy factory floor just by reading about it. It has to experience the physical world. And that’s why what Humyn Labs is building is so important. They’re turning real human experiences into model-ready data across sound, sight, motion and touch, so Physical AI can learn from the world as it actually exists, not some perfectly controlled laboratory. The next breakthrough in robotics may not come from making the model bigger. It may come from giving the model better experiences to learn from. That’s the human layer Physical AI has been missing. 🟧

Bahdfella™ ❤️‍🔥

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Very excited to share what our research team at Covariant has been working on: RFM-1, our latest Robotics Foundation Model. Built on top of high-quality multimodal data the Covariant robotics fleet has collected over the years, this project embodies our commitment to pushing the boundaries of how AI can interact with and understand the physical world. We're eager to share this milestone with the community and invite feedback and collaboration. Read more at

Rocky Duan

31,901 Aufrufe • vor 2 Jahren

Today, we’re releasing a significant upgrade to our specialized reasoning mode, Gemini 3 Deep Think. Deep Think is built to drive practical applications, enabling researchers to interpret complex data and engineers to model physical systems through code. With the updated Deep Think, you can turn a sketch into a 3D-printable reality. Deep Think analyzes the drawing, builds the complex shape, and generates a file so you can create the physical object with 3D printing. This is rolling out now to Google AI Ultra subscribers. Select the "Deep Think" option in the tools menu to get started. Learn more here:
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Today, we’re releasing a significant upgrade to our specialized reasoning mode, Gemini 3 Deep Think. Deep Think is built to drive practical applications, enabling researchers to interpret complex data and engineers to model physical systems through code. With the updated Deep Think, you can turn a sketch into a 3D-printable reality. Deep Think analyzes the drawing, builds the complex shape, and generates a file so you can create the physical object with 3D printing. This is rolling out now to Google AI Ultra subscribers. Select the "Deep Think" option in the tools menu to get started. Learn more here:

Google Gemini

5,061,045 Aufrufe • vor 8 Monaten

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.
1:00

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Neuracore

40,810 Aufrufe • vor 11 Monaten

At microagi we want to put Europe at the front of physical and embodied AI. That’s why we’ve created the microagi research fellowship. Fellows can build in our lab in Zurich or Munich, work with our team, our datasets and our partners like Unitree, Nvidia, and Google. If you know someone who should be focussing on physical AI without any distractions, tag them, if you’re the first to tag someone who is accepted we’ll send you 10.000 USD
1:51

At microagi we want to put Europe at the front of physical and embodied AI. That’s why we’ve created the microagi research fellowship. Fellows can build in our lab in Zurich or Munich, work with our team, our datasets and our partners like Unitree, Nvidia, and Google. If you know someone who should be focussing on physical AI without any distractions, tag them, if you’re the first to tag someone who is accepted we’ll send you 10.000 USD

Andrew Jefferson

36,087 Aufrufe • vor 4 Monaten