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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 views • 8 months ago •via X (Twitter)

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Pi Ventures Joins Hack VC To Back AI Robotics Startup On Base Axis Robotics (Axis Robotics) has raised a $12 million seed round led by Hack VC, with participation from Pi Network (Pi Network) Ventures, Nomad Capital, 10K Ventures, and other angel investors. The company is building a data engine for Physical AI that combines simulation, real world data capture, and human feedback to generate scalable robotics datasets. Axis said the funding will accelerate development of its global human in the loop data engine. Base (Base APAC) congratulated the team, calling Axis one of the leading scalable Physical AI and robotics platforms building on the network.

Pi Ventures Joins Hack VC To Back AI Robotics Startup On Base Axis Robotics (Axis Robotics) has raised a $12 million seed round led by Hack VC, with participation from Pi Network (Pi Network) Ventures, Nomad Capital, 10K Ventures, and other angel investors. The company is building a data engine for Physical AI that combines simulation, real world data capture, and human feedback to generate scalable robotics datasets. Axis said the funding will accelerate development of its global human in the loop data engine. Base (Base APAC) congratulated the team, calling Axis one of the leading scalable Physical AI and robotics platforms building on the network.

BSCN

51,740 views • 1 month ago

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✌️📷

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✌️📷

Axis Robotics

28,096 views • 8 months ago

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!

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!

GAIB

391,330 views • 11 months ago

Announcing our commercial partnership with Booster Robotics Booster builds humanoid robot hardware, OS, and developer tools to make humanoid robots more affordable, reliable, and practical. The partnership centers on using simulation to multiply the value of real-robot data—expanding teleoperation demonstrations into scalable training data across diverse tasks and scenes. This joint effort powers sim-real co-training and foundation-model development, accelerating progress from hardware iteration to deployable robot policies. Together, we're building simulation‑powered data infrastructure for Physical AI — making scalable training data accessible to model developers and the broader robotics ecosystem.

Announcing our commercial partnership with Booster Robotics Booster builds humanoid robot hardware, OS, and developer tools to make humanoid robots more affordable, reliable, and practical. The partnership centers on using simulation to multiply the value of real-robot data—expanding teleoperation demonstrations into scalable training data across diverse tasks and scenes. This joint effort powers sim-real co-training and foundation-model development, accelerating progress from hardware iteration to deployable robot policies. Together, we're building simulation‑powered data infrastructure for Physical AI — making scalable training data accessible to model developers and the broader robotics ecosystem.

Axis Robotics

69,466 views • 1 month ago

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.

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.

Skild AI

258,685 views • 5 months ago

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.

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.

Tuo Liu

17,573 views • 6 months ago

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.

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

677,621 views • 8 months ago

📍 Introducing Tagger — The Decentralized #AI Data Layer ➡️ Tagger is a full-stack DeCorp platform revolutionizing how high-quality training data is created, authenticated, and traded. - AI-powered labeling tools that turn anyone into a pro annotator - Instant rewards in $TAG via smart contracts - Secure data authentication & ownership - Open marketplace for specialized datasets (healthcare, autonomous driving, robotics, agriculture & more) 🔗 CA ( $BNB ) : 0x208bf3e7da9639f1eaefa2de78c23396b0682025 🌐 🦾 The AI revolution runs on data. Tagger makes it permissionless, transparent, and global.

📍 Introducing Tagger — The Decentralized #AI Data Layer ➡️ Tagger is a full-stack DeCorp platform revolutionizing how high-quality training data is created, authenticated, and traded. - AI-powered labeling tools that turn anyone into a pro annotator - Instant rewards in $TAG via smart contracts - Secure data authentication & ownership - Open marketplace for specialized datasets (healthcare, autonomous driving, robotics, agriculture & more) 🔗 CA ( $BNB ) : 0x208bf3e7da9639f1eaefa2de78c23396b0682025 🌐 🦾 The AI revolution runs on data. Tagger makes it permissionless, transparent, and global.

Crypto Holding™ 💎

113,153 views • 1 month ago

“An 8 year old today, they're not going to need a driver's license" ... "autonomous and robotics is the future" - Dan Ives

“An 8 year old today, they're not going to need a driver's license" ... "autonomous and robotics is the future" - Dan Ives

Evan

48,805 views • 7 months ago

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 -

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

14,523 views • 3 months ago

🚀 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

🚀 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

36,229 views • 1 year ago

How about designing code that can help shape the future of in-space servicing infrastructure? Registration for the NASA Space Roboticist Challenge is officially open! Compete for allocated time on the FFR mission’s robotic arm. Let’s upgrade robotics in space together:

How about designing code that can help shape the future of in-space servicing infrastructure? Registration for the NASA Space Roboticist Challenge is officially open! Compete for allocated time on the FFR mission’s robotic arm. Let’s upgrade robotics in space together:

NASA Technology

42,959 views • 1 month ago

This is the kind of robotics future we want. Where would you take one first? And what would stop you from buying one?

This is the kind of robotics future we want. Where would you take one first? And what would stop you from buying one?

Techniahqrobot | humanoid robots

27,562 views • 1 month ago

MasterBOT is officially integrating with NVIDIA Isaac Lab NVIDIA’s robotics simulation platform built for developing and training autonomous machines This brings $BOT one step closer to powering the future of decentralized Robotics AI

MasterBOT is officially integrating with NVIDIA Isaac Lab NVIDIA’s robotics simulation platform built for developing and training autonomous machines This brings $BOT one step closer to powering the future of decentralized Robotics AI

MasterBOT

103,938 views • 10 months ago

Rare-event discovery for robots training is what we’ve been working on lately How would a robot react to an unusual event? What if a firefighter drone won’t be able to choke a fire (like on the video below)? Robo-doctors use cases? How to mass-produce these situations to train robots to see & react the best way? I think that open-sourced, world models like LTX can become the solution for such training process. And they might become the differentiator for the future of robotics. Of course, I’m not an expert in robotics and ML, but this topic makes me curious - and I’m curious about your thoughts

Rare-event discovery for robots training is what we’ve been working on lately How would a robot react to an unusual event? What if a firefighter drone won’t be able to choke a fire (like on the video below)? Robo-doctors use cases? How to mass-produce these situations to train robots to see & react the best way? I think that open-sourced, world models like LTX can become the solution for such training process. And they might become the differentiator for the future of robotics. Of course, I’m not an expert in robotics and ML, but this topic makes me curious - and I’m curious about your thoughts

AmirMušić

41,839 views • 1 month ago

Mondo is the most adorable companion robot. It’s safe around kids, can track and follow a person, and capture high-resolution action videos. Mondo is also doing frontier research in humanoid Video-Action models built on a world model backbone: I have no doubt we’ll be hearing this name a lot in consumer robotics. Thanks Mondo Robotics Shuo Yang for the tour.

Mondo is the most adorable companion robot. It’s safe around kids, can track and follow a person, and capture high-resolution action videos. Mondo is also doing frontier research in humanoid Video-Action models built on a world model backbone: I have no doubt we’ll be hearing this name a lot in consumer robotics. Thanks Mondo Robotics Shuo Yang for the tour.

The Humanoid Hub

27,765 views • 2 months ago

AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

Perceptron Network

75,377 views • 5 months ago