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Open-Sourced Robotics Datasets Have Exploded This Year, Turning The Field Into A More Scalable And Collaborative Ecosystem. We can expect major breakthroughs in the very near future; the data for robotics is exploding!

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14,321 görüntüleme • 6 ay önce •via X (Twitter)

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A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷
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A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

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We invite you to sign The Promise of the Fremen, a commitment to build the future of AI & Robotics together, onchain. We believe in crypto, AI, Robotics, open access and responsibility. We sign this manifesto to register it for eternity. Sign with us!
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We invite you to sign The Promise of the Fremen, a commitment to build the future of AI & Robotics together, onchain. We believe in crypto, AI, Robotics, open access and responsibility. We sign this manifesto to register it for eternity. Sign with us!

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2026 is also going to be the year of data collection for robotics.
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2026 is also going to be the year of data collection for robotics.

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Robotics is a data problem. Today, we’re partnering with ABB Robotics, Universal Robots, and NVIDIA Robotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines. This will help us build the world’s biggest data flywheel for physical AI.
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Robotics is a data problem. Today, we’re partnering with ABB Robotics, Universal Robots, and NVIDIA Robotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines. This will help us build the world’s biggest data flywheel for physical AI.

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253,736 görüntüleme • 3 ay önce

Many robotics companies should be thanking Unitree. They've set a very high bar and definitely accelerated the entire robotics field globally. Just like Apple did for smartphones and Tesla for EVs.
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Many robotics companies should be thanking Unitree. They've set a very high bar and definitely accelerated the entire robotics field globally. Just like Apple did for smartphones and Tesla for EVs.

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Predicting how the environment changes over time is a key task in robotics, enabling all kinds of downstream tasks. Trace Anything is a very interesting look at how you can train models which predict the trajectory each point will take in the future -- you can use this for robot manipulation, video prediction, and more. Learn more ->
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Predicting how the environment changes over time is a key task in robotics, enabling all kinds of downstream tasks. Trace Anything is a very interesting look at how you can train models which predict the trajectory each point will take in the future -- you can use this for robot manipulation, video prediction, and more. Learn more ->

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teleoperator kicking himself over not programming Asimov's Laws of Robotics... Worth considering if we should base today's reality on the science fiction musings from the past. Testing with humanoid robots is a very interesting new challenge. Historically robots have been confined to keep out zones with rigid walls and laser barriers, lock-out/tag-out, etc. This is the easy way to ensure people don't get hurt by very capable machines. But the more we integrate humanoid robotics into collaborative roles with humans the more we need to remove those barriers by nature of the work.
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teleoperator kicking himself over not programming Asimov's Laws of Robotics... Worth considering if we should base today's reality on the science fiction musings from the past. Testing with humanoid robots is a very interesting new challenge. Historically robots have been confined to keep out zones with rigid walls and laser barriers, lock-out/tag-out, etc. This is the easy way to ensure people don't get hurt by very capable machines. But the more we integrate humanoid robotics into collaborative roles with humans the more we need to remove those barriers by nature of the work.

Wes

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THE FUTURE IS NOW #adultswim #robotics
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THE FUTURE IS NOW #adultswim #robotics

adult swim

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Sensei Robotics (YC S24) helps robotics companies generate high-quality robotics training data at scale. Their platform collects human demonstration data at a tenth of the cost and twice the speed of current approaches.
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Sensei Robotics (YC S24) helps robotics companies generate high-quality robotics training data at scale. Their platform collects human demonstration data at a tenth of the cost and twice the speed of current approaches.

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The future of robotics is human.
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The future of robotics is human.

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"Automate Earth, Enable Space" Over the last two weeks, we've explored how robotics can transform the terra-space industry. We're incredibly grateful to Meagan Murphy Crawford , Jim Thomas , and Sean D. Graham for introducing us to key industry leaders and facilitating our tour of the US Space Force facility at Cape Canaveral. With a deeper understanding of the space sector’s needs, we see a growing opportunity for Earth-based robotics to play a pivotal role in supporting space advancements. The intersection of robotics and space is not just a possibility; it’s essential for the future of both industries. #RoboticsForSpace #TerraSpace #AutomateEarth #FutureOfSpace #InnovationInAction #Robotics NVIDIA Robotics
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“An 8 year old today, they're not going to need a driver's license" ... "autonomous and robotics is the future" - Dan Ives

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The future of robotics is fast approaching 🤯
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The future of robotics is fast approaching 🤯

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Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵
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Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵

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We are excited to see researchers and students continue pushing open-source robotics forward with Open Ant 🐜 The project uses XC430 and XM430 actuators to bring agile locomotion to life. Proud to support innovation in robotics education, research, and biomimetic systems worldwide. Explore the Openmind Research Institute Github for more information -
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We are excited to see researchers and students continue pushing open-source robotics forward with Open Ant 🐜 The project uses XC430 and XM430 actuators to bring agile locomotion to life. Proud to support innovation in robotics education, research, and biomimetic systems worldwide. Explore the Openmind Research Institute Github for more information -

ROBOTIS AMERICA

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🚨HUGE: OPENMIND AGI HAS ANNOUNCED SIX MAJOR ROBOTICS PARTNERSHIPS This week alone, OpenMind, which has direct investment from Pi Network's $PI Ventures, has announced six partnerships with humanoid robotics companies... Fourier Booster Robotics LimX Dynamics DEEP Robotics AGIBOT UBTECH Robotics It plans to announce four more before the end of the week.
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🚨HUGE: OPENMIND AGI HAS ANNOUNCED SIX MAJOR ROBOTICS PARTNERSHIPS This week alone, OpenMind, which has direct investment from Pi Network's $PI Ventures, has announced six partnerships with humanoid robotics companies... Fourier Booster Robotics LimX Dynamics DEEP Robotics AGIBOT UBTECH Robotics It plans to announce four more before the end of the week.

BSCN

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🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned
0:18

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

32,085 görüntüleme • 1 yıl önce

What you’re seeing isn’t science fiction, it’s the future of robotics being trained in real time MasterBOT’s simulation engine accelerates AI learning without hardware, powered by community and data Welcome to the new era of Robotics x web3 $BOT
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What you’re seeing isn’t science fiction, it’s the future of robotics being trained in real time MasterBOT’s simulation engine accelerates AI learning without hardware, powered by community and data Welcome to the new era of Robotics x web3 $BOT

MasterBOT

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The future of robotics has a gentle touch
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The future of robotics has a gentle touch

S.T.E.M Explorer

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The world's open robotics lab is entering its next phase. Contribute robotics work. Level up your lab access. Applications open soon.
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The world's open robotics lab is entering its next phase. Contribute robotics work. Level up your lab access. Applications open soon.

BitRobot 🦾

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