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If you’ve been ignoring Axis Robotics because it looks like another random points farm, read this. In the last 48 hours, Unitree said robotics is approaching its "ChatGPT moment", while the chairman of ACE Robotics believes it could happen by the end of 2027. But for robots to reach...

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Pranjal Bora 🧭

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31,455 görüntüleme • 1 ay önce •via X (Twitter)

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MAKING AXIS TASKS FEEL EASY 👀🤖 I made a quick video showing how to complete an Axis Robotics task step by step. The task gives you a clear goal, steps, controls and camera views to guide you. In this one, the goal is simple, Open the trash bin, pick up the onion, and place it inside. 🧅 Just follow the instructions, control the robot arm and complete the task. What I like is that even beginners can understand what they need to do. But the interesting part is what happens with these interactions after we complete them. Each completed task can contribute valuable robot training data, helping Physical Ai systems learn how to handle different objects and environments. Small tasks from the community can become useful data for teaching robots how to interact with the real world. That’s what makes Axis interesting to me.
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MAKING AXIS TASKS FEEL EASY 👀🤖 I made a quick video showing how to complete an Axis Robotics task step by step. The task gives you a clear goal, steps, controls and camera views to guide you. In this one, the goal is simple, Open the trash bin, pick up the onion, and place it inside. 🧅 Just follow the instructions, control the robot arm and complete the task. What I like is that even beginners can understand what they need to do. But the interesting part is what happens with these interactions after we complete them. Each completed task can contribute valuable robot training data, helping Physical Ai systems learn how to handle different objects and environments. Small tasks from the community can become useful data for teaching robots how to interact with the real world. That’s what makes Axis interesting to me.

SufianXFN

14,419 görüntüleme • 1 ay önce

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.
0:16

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.

The Daily Ai

33,341 görüntüleme • 1 ay önce

Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at hello@qualiastudios.dev More soon
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Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at [email protected] More soon

Qualia

88,038 görüntüleme • 4 ay önce

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
0:10

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 görüntüleme • 2 ay önce

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✌️📷
0:13

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 görüntüleme • 9 ay önce

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
0:16

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 görüntüleme • 1 yıl önce

Most people still think the internet is free. It isn’t. Every click, search, scroll, and workflow is training data. Users generate it. Platforms keep it. AI gets smarter. You get nothing. Action Model flips that. The browser extension doesn’t ask you to “farm tasks.” It verifies real actions while you use the product ... then lets you participate in the value those actions create. That’s the model. Train it. Earn it. Own it. And right now there’s a live proof of that model: NeoSoul x ActionFi $5K still on the table Create your first agent. Meet the criteria. Get verified. No vanity clicks. No empty follows. If the extension is already on, you can start now. If it isn’t, you’re watching a closing window from the sidelines. This is the last stretch. Not later. Now. 👉 Action Model #ActionFi #NeoSoul
0:15

Most people still think the internet is free. It isn’t. Every click, search, scroll, and workflow is training data. Users generate it. Platforms keep it. AI gets smarter. You get nothing. Action Model flips that. The browser extension doesn’t ask you to “farm tasks.” It verifies real actions while you use the product ... then lets you participate in the value those actions create. That’s the model. Train it. Earn it. Own it. And right now there’s a live proof of that model: NeoSoul x ActionFi $5K still on the table Create your first agent. Meet the criteria. Get verified. No vanity clicks. No empty follows. If the extension is already on, you can start now. If it isn’t, you’re watching a closing window from the sidelines. This is the last stretch. Not later. Now. 👉 Action Model #ActionFi #NeoSoul

Trathoa

19,704 görüntüleme • 1 ay önce

What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.
0:16

What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.

Rerun

11,355 görüntüleme • 1 ay önce

The Mocaverse (old account) Shard Rush dashboard looks really complicated.😵‍💫 It's not. 😂 The current Aethir campaign only lasts one week. If you want an airdrop of 1,260 $ATH, here's my advice: MAX OUT YOUR GENERATION RATE ASAP. What does that even mean? Lemme break it down👇 Mocaverse built out a pretty cool system to distribute rewards to participants (cough farmers cough). In its first 3 seasons, Shard Rush was used to basically distribute $MOCA presale waitlist codes; now they're using it for a corporate partner — hyped DePIN project Aethir. There are 10,000 lots of 1,260 $ATH each that will be raffled off to MocaID holders that have earned at least 60 Realm Points during the campaign period. (Moca NFT holders have a separate pool of $ATH to split.) Each Shard is a raffle ticket, so you want as many as possible. The mechanics for Shards is that you'll mine a certain amount every hour; so the earlier you unlock the maximum of +7 Shards/hour, the more Shards you'll accumulate in total. This matters A LOT since this campaign only runs for 1 week — which means if you complete it on the second day and get 6 full days of Shards (6 x 24 x 7 = 1,008), that gives you a 600% more likely chance to receive that airdrop than someone who gets in on the last day (24 x 7 = 168). Note that there are over 1.46 million MocaID holders, so 10,000 accounts for the airdrop is still not quite "oversubscribed". Some of the previous campaign numbers suggested 200k or so active accounts, so... that's your competition I guess. Since you need to earn 210 Realm Points to max Shards, after completing a few easy social tasks I had to re-download 👾 Arc8 by GAMEE and complete quite a few of the Missions that included playing 5 different games & getting to 300,000 total points in Moca Racer. The upside is that the games are decently fun and you can earn all the Realm Points you need for free; the time cost was roughly 30-40 minutes in game time (that I spent in transit and while waiting for dinner). A few extra notes: 🌟 Realm Points will be used to determine $MOCA airdrop amounts for MocaID holders. Even though the presale has ended, we haven't seen TGE yet — if you want your cut of the airdrop, there's still time to farm. 🌟 Current iterations of Shard Rush are free, but in the future they'll cost Realm Points (I guess having a Points sink/tax is a good idea). Stocking up on Realm Points now when tasks are easier seems to be a good idea. If you somehow don't have a MocaID yet, here are some of my referral codes: 4ZBZBS9KS5 A884LCY9DT N1AARWT3BK QSL7CB4MU7 1LMB8FGQNN AA156EZPWT 3SVU7GA0EV TMFURCLBDP Link to sign up is in the tweet below. If the codes don't work, somebody else has used them. Last but not least: if you've made it all the way to the end of this tweet, thank you so much for your support. I truly appreciate it 🫡
0:16

The Mocaverse (old account) Shard Rush dashboard looks really complicated.😵‍💫 It's not. 😂 The current Aethir campaign only lasts one week. If you want an airdrop of 1,260 $ATH, here's my advice: MAX OUT YOUR GENERATION RATE ASAP. What does that even mean? Lemme break it down👇 Mocaverse built out a pretty cool system to distribute rewards to participants (cough farmers cough). In its first 3 seasons, Shard Rush was used to basically distribute $MOCA presale waitlist codes; now they're using it for a corporate partner — hyped DePIN project Aethir. There are 10,000 lots of 1,260 $ATH each that will be raffled off to MocaID holders that have earned at least 60 Realm Points during the campaign period. (Moca NFT holders have a separate pool of $ATH to split.) Each Shard is a raffle ticket, so you want as many as possible. The mechanics for Shards is that you'll mine a certain amount every hour; so the earlier you unlock the maximum of +7 Shards/hour, the more Shards you'll accumulate in total. This matters A LOT since this campaign only runs for 1 week — which means if you complete it on the second day and get 6 full days of Shards (6 x 24 x 7 = 1,008), that gives you a 600% more likely chance to receive that airdrop than someone who gets in on the last day (24 x 7 = 168). Note that there are over 1.46 million MocaID holders, so 10,000 accounts for the airdrop is still not quite "oversubscribed". Some of the previous campaign numbers suggested 200k or so active accounts, so... that's your competition I guess. Since you need to earn 210 Realm Points to max Shards, after completing a few easy social tasks I had to re-download 👾 Arc8 by GAMEE and complete quite a few of the Missions that included playing 5 different games & getting to 300,000 total points in Moca Racer. The upside is that the games are decently fun and you can earn all the Realm Points you need for free; the time cost was roughly 30-40 minutes in game time (that I spent in transit and while waiting for dinner). A few extra notes: 🌟 Realm Points will be used to determine $MOCA airdrop amounts for MocaID holders. Even though the presale has ended, we haven't seen TGE yet — if you want your cut of the airdrop, there's still time to farm. 🌟 Current iterations of Shard Rush are free, but in the future they'll cost Realm Points (I guess having a Points sink/tax is a good idea). Stocking up on Realm Points now when tasks are easier seems to be a good idea. If you somehow don't have a MocaID yet, here are some of my referral codes: 4ZBZBS9KS5 A884LCY9DT N1AARWT3BK QSL7CB4MU7 1LMB8FGQNN AA156EZPWT 3SVU7GA0EV TMFURCLBDP Link to sign up is in the tweet below. If the codes don't work, somebody else has used them. Last but not least: if you've made it all the way to the end of this tweet, thank you so much for your support. I truly appreciate it 🫡

MERC subvert

45,312 görüntüleme • 2 yıl önce

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:
0:11

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 görüntüleme • 10 ay önce

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?
0:15

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 görüntüleme • 7 ay önce

40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.
0:11

40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.

Future Memo

18,925 görüntüleme • 27 gün önce

Multi-robot learning is getting a serious boost! 📚 Researchers have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:16

Multi-robot learning is getting a serious boost! 📚 Researchers have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

38,997 görüntüleme • 10 ay önce

Comprehensive Guide to Maximize Your Points in the Mode Airdrop: Strategies and Tips 🟡 Mode's airdrop is available, but earning points is not just about volume. Here's what really matters when it comes to increasing your points: Precheck: • Past activity, NFT holdings, and participation in Mode campaigns get you points. (Testnet participants, early NFT holders, and Degen Score Beacon holders) TVL: • Bridge your assets to Mode and keep them there for maximum points. The longer, the better! • Fees generated matter more than just volume, so avoid botting. Referrals: • Can't bring big TVL? Refer others who do! You earn 16% of their points (TVL & fees). Example: Refer someone who generates 10,000 points, you get 1,600. Quests & Ecosystem: • Complete on-chain quests and use approved dapps for points. More dapps coming soon! • Provide liquidity to approved partners and earn double the points for TVL. Bonus Points: • Get a Mode NS name for points and future ecosystem benefits. • Keep an eye out for partner airdrops (separate from Mode points). How not to earn points • Volume doesn't matter. Focus on fees generated. • Buying Mode NFTs now won't get you points. They might be useful later, but for now... nah. • Making tons of wallets is pointless. 1 wallet with high TVL beats 100 with low TVL. • Depositing $10 and doing nothing won't work. Be active, bring value, and support the ecosystem. Remember, this is only Airdrop 1. Mode is building a thriving ecosystem and your active participation is important. Participate, earn points and be part of Mode. If you have not yet participated in Mode 🟡 airdrop campaign, do it now! You will need to enter through a referral link: Or use my code: 2uyMS8 If You Know You Mode, Good luck Chad!
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Comprehensive Guide to Maximize Your Points in the Mode Airdrop: Strategies and Tips 🟡 Mode's airdrop is available, but earning points is not just about volume. Here's what really matters when it comes to increasing your points: Precheck: • Past activity, NFT holdings, and participation in Mode campaigns get you points. (Testnet participants, early NFT holders, and Degen Score Beacon holders) TVL: • Bridge your assets to Mode and keep them there for maximum points. The longer, the better! • Fees generated matter more than just volume, so avoid botting. Referrals: • Can't bring big TVL? Refer others who do! You earn 16% of their points (TVL & fees). Example: Refer someone who generates 10,000 points, you get 1,600. Quests & Ecosystem: • Complete on-chain quests and use approved dapps for points. More dapps coming soon! • Provide liquidity to approved partners and earn double the points for TVL. Bonus Points: • Get a Mode NS name for points and future ecosystem benefits. • Keep an eye out for partner airdrops (separate from Mode points). How not to earn points • Volume doesn't matter. Focus on fees generated. • Buying Mode NFTs now won't get you points. They might be useful later, but for now... nah. • Making tons of wallets is pointless. 1 wallet with high TVL beats 100 with low TVL. • Depositing $10 and doing nothing won't work. Be active, bring value, and support the ecosystem. Remember, this is only Airdrop 1. Mode is building a thriving ecosystem and your active participation is important. Participate, earn points and be part of Mode. If you have not yet participated in Mode 🟡 airdrop campaign, do it now! You will need to enter through a referral link: Or use my code: 2uyMS8 If You Know You Mode, Good luck Chad!

ETHachi Uchiha | Crypto DEGENius

17,392 görüntüleme • 2 yıl önce

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.
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I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

Miyu Horiuchi

90,718 görüntüleme • 8 ay önce

THE FIRST GENERATION TO GROW UP WITH HUMANOID ROBOTS IS ALREADY HERE. And that could matter more than any robot demo. Look at this scene. A humanoid standing outside a school. Children walking around it. Watching it. Interacting with it. For adults, a machine like this still looks futuristic. For a child growing up around them? It could eventually look completely normal. Think about how quickly that changes a market. A generation ago, having a computer inside your home was unusual. Then children grew up with computers. The same happened with smartphones. Then tablets. Then AI. Technology stops feeling revolutionary when an entire generation has never known a world without it. Humanoids could be next. Imagine children growing up seeing robots in schools. Stores. Airports. Hospitals. Hotels. Homes. They won’t ask whether humans should interact with robots. They’ll already be doing it. And that matters economically. Because one of the biggest barriers to consumer robotics isn’t just price. It’s trust. People need to feel comfortable allowing an autonomous machine into their personal space. Inside their homes. Around their families. Around their children. That kind of acceptance can take years. Sometimes generations. But children who grow up around humanoids won’t need the same adjustment. To them, asking a robot to carry something might feel as normal as asking Alexa a question feels today. And once that happens, the addressable market changes dramatically. A robot isn’t just industrial equipment anymore. It becomes a household product. A service platform. An assistant. Potentially even something families upgrade every few years. First robot at school. First robot at the mall. First robot at work. Eventually… first robot at home. The companies building humanoids today aren’t only competing for today’s customers. They may be competing to become familiar to an entire generation of future customers. Because the biggest sign that humanoid robots have won won’t be people staring at them. It’ll be children walking past one without thinking twice.
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THE FIRST GENERATION TO GROW UP WITH HUMANOID ROBOTS IS ALREADY HERE. And that could matter more than any robot demo. Look at this scene. A humanoid standing outside a school. Children walking around it. Watching it. Interacting with it. For adults, a machine like this still looks futuristic. For a child growing up around them? It could eventually look completely normal. Think about how quickly that changes a market. A generation ago, having a computer inside your home was unusual. Then children grew up with computers. The same happened with smartphones. Then tablets. Then AI. Technology stops feeling revolutionary when an entire generation has never known a world without it. Humanoids could be next. Imagine children growing up seeing robots in schools. Stores. Airports. Hospitals. Hotels. Homes. They won’t ask whether humans should interact with robots. They’ll already be doing it. And that matters economically. Because one of the biggest barriers to consumer robotics isn’t just price. It’s trust. People need to feel comfortable allowing an autonomous machine into their personal space. Inside their homes. Around their families. Around their children. That kind of acceptance can take years. Sometimes generations. But children who grow up around humanoids won’t need the same adjustment. To them, asking a robot to carry something might feel as normal as asking Alexa a question feels today. And once that happens, the addressable market changes dramatically. A robot isn’t just industrial equipment anymore. It becomes a household product. A service platform. An assistant. Potentially even something families upgrade every few years. First robot at school. First robot at the mall. First robot at work. Eventually… first robot at home. The companies building humanoids today aren’t only competing for today’s customers. They may be competing to become familiar to an entire generation of future customers. Because the biggest sign that humanoid robots have won won’t be people staring at them. It’ll be children walking past one without thinking twice.

Mag1strY0da

10,149 görüntüleme • 12 gün önce

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:
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Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:

Jim Fan

265,061 görüntüleme • 3 yıl önce

Humanity verification for Theoriq pre-claim registration is now LIVE on Authena. If you’re eligible for Theoriq’s testnet rewards, you’ll need to complete a quick Proof of Humanity check and earn 40 Humanity Points to qualify. Here’s how it works: - Start at Theoriq’s pre-claim registration page and select "I'm a Testnet User": Connect your wallet to check your eligibility. - If you’re eligible, go to Theoriq’s Authena page: Sign up and start building your Humanity Score by connecting real credentials. - Every credential adds points - X, Discord, Farcaster, TG premium, ENS holder, CEX KYC ZK proofs and more. You only need 40 points to qualify. - Once you hit 40+, mint your Theoriq Authena NFT. - Return to the Theoriq registration page → click Refresh Status. Done. You’re officially human-verified for the pre-claim. Verify & mint now:
0:18

Humanity verification for Theoriq pre-claim registration is now LIVE on Authena. If you’re eligible for Theoriq’s testnet rewards, you’ll need to complete a quick Proof of Humanity check and earn 40 Humanity Points to qualify. Here’s how it works: - Start at Theoriq’s pre-claim registration page and select "I'm a Testnet User": Connect your wallet to check your eligibility. - If you’re eligible, go to Theoriq’s Authena page: Sign up and start building your Humanity Score by connecting real credentials. - Every credential adds points - X, Discord, Farcaster, TG premium, ENS holder, CEX KYC ZK proofs and more. You only need 40 points to qualify. - Once you hit 40+, mint your Theoriq Authena NFT. - Return to the Theoriq registration page → click Refresh Status. Done. You’re officially human-verified for the pre-claim. Verify & mint now:

Authena

10,650 görüntüleme • 10 ay önce