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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,... show more

Qualia

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88,190 görüntüleme • 4 ay önce •via X (Twitter)

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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.

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

Everyone calls us a robotics company. We are a stone company. The robots exist because Britain no longer has the armies of carvers and stonemasons who built Blenheim, Castle Howard, and Westminster. We are rebuilding that capacity.

Everyone calls us a robotics company. We are a stone company. The robots exist because Britain no longer has the armies of carvers and stonemasons who built Blenheim, Castle Howard, and Westminster. We are rebuilding that capacity.

Arka Serezh

87,898 görüntüleme • 4 ay önce

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,517 görüntüleme • 11 ay önce

Robots are taking over construction sites! Wall finishing is one of the most labor-intensive jobs in construction. Applying putty, sanding surfaces, and painting still require hours of repetitive manual work. China's HX Precision has developed the HX33-W, a 3-in-1 wall finishing robot that handles all three tasks. The robot automatically applies and smooths putty, sands walls, and paints surfaces, navigating construction sites without continuous manual control. According to the manufacturer, it delivers 4–6× higher productivity than traditional manual work, reaches heights of up to 3.3 meters, and operates for 6–8 hours on battery power. What's interesting is how specialized these machines are becoming. Instead of trying to automate an entire construction site, they focus on repetitive tasks where labor costs, consistency, and productivity matter. Construction has been notoriously difficult to automate. Robots like this could start changing that. ~~ 🤖 Interested in robotics and AI? →

Robots are taking over construction sites! Wall finishing is one of the most labor-intensive jobs in construction. Applying putty, sanding surfaces, and painting still require hours of repetitive manual work. China's HX Precision has developed the HX33-W, a 3-in-1 wall finishing robot that handles all three tasks. The robot automatically applies and smooths putty, sands walls, and paints surfaces, navigating construction sites without continuous manual control. According to the manufacturer, it delivers 4–6× higher productivity than traditional manual work, reaches heights of up to 3.3 meters, and operates for 6–8 hours on battery power. What's interesting is how specialized these machines are becoming. Instead of trying to automate an entire construction site, they focus on repetitive tasks where labor costs, consistency, and productivity matter. Construction has been notoriously difficult to automate. Robots like this could start changing that. ~~ 🤖 Interested in robotics and AI? →

Ilir Aliu

16,300 görüntüleme • 16 gün önce

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 that level, they need a crazy amount of training data. That’s exactly what Axis is building. You control simulated robots directly from your browser, complete simple tasks and earn points. Those movements also help create data that can be used to train real robots. And this isn’t some tiny experiment anymore: - $12M raised - 123K+ contributors - 3M+ robot trajectories - Community data already used to train a real robot So yeah, we’re basically farming a potential airdrop while teaching our future robot servants how to work 😂 If you haven’t started yet, it’s completely free. You only need a tiny amount of gas on Base to sign your completed tasks. ✅ Start farming Axis points: Important: Sign every completed task from the History page, otherwise you won’t receive the points.

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 that level, they need a crazy amount of training data. That’s exactly what Axis is building. You control simulated robots directly from your browser, complete simple tasks and earn points. Those movements also help create data that can be used to train real robots. And this isn’t some tiny experiment anymore: - $12M raised - 123K+ contributors - 3M+ robot trajectories - Community data already used to train a real robot So yeah, we’re basically farming a potential airdrop while teaching our future robot servants how to work 😂 If you haven’t started yet, it’s completely free. You only need a tiny amount of gas on Base to sign your completed tasks. ✅ Start farming Axis points: Important: Sign every completed task from the History page, otherwise you won’t receive the points.

Pranjal Bora 🧭

31,681 görüntüleme • 2 ay önce

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 ->

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 ->

Chris Paxton

58,763 görüntüleme • 10 ay önce

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,404 görüntüleme • 2 ay önce

Hardware is a big constraint in robotics, that's why we don't think the answer will be increasingly larger VLM models as action backbones. We tried to solve that issue with WorldDiT, which is at the pareto frontier for size-performance. Now I want to see how far we can scale it without losing this tradeoff. Here's a simulation demo.

Hardware is a big constraint in robotics, that's why we don't think the answer will be increasingly larger VLM models as action backbones. We tried to solve that issue with WorldDiT, which is at the pareto frontier for size-performance. Now I want to see how far we can scale it without losing this tradeoff. Here's a simulation demo.

Bidhan Roy

11,405 görüntüleme • 3 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

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

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via

Stephen James

16,951 görüntüleme • 1 yıl önce

I'll never forget how many people didn't understand what I was doing with this proof of concept, and cried that it was impossible for a robot quadruped to handle recoil, and stay on target. Look where we are today. Ghost Robotics vision 60 on the range!

I'll never forget how many people didn't understand what I was doing with this proof of concept, and cried that it was impossible for a robot quadruped to handle recoil, and stay on target. Look where we are today. Ghost Robotics vision 60 on the range!

KF

1,163,306 görüntüleme • 3 ay önce

Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!

Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!

Yunzhu Li

11,344 görüntüleme • 5 ay önce

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

Zubair Irshad

20,518 görüntüleme • 1 yıl önce

$The good news: you can mid-train yourself to the frontier! At a fraction of the cost! Without distillation! What we did: We took Qwen3.5 14B and mid- and post-trained it to beat every other open model and perform as well as the very best frontier models. This is no simple toy task or format fixing, we are training the model to understand and reason over DNA sequences. Bio is the next frontier for AI and universal access will be crucial to make progress. The fun part: because we train the model on both natural language and DNA sequences you can actually 'chat to your DNA': Ask it questions, ponder the impact of mutations, or ask it to generate a small sequence. We'll soon see the frontier labs move away from math and cyber towards bio. It already started, and we believe researchers should have access to capable models. We are releasing the full recipe, so you can do it, too! Model: Blog:$

The good news: you can mid-train yourself to the frontier! At a fraction of the cost! Without distillation! What we did: We took Qwen3.5 14B and mid- and post-trained it to beat every other open model and perform as well as the very best frontier models. This is no simple toy task or format fixing, we are training the model to understand and reason over DNA sequences. Bio is the next frontier for AI and universal access will be crucial to make progress. The fun part: because we train the model on both natural language and DNA sequences you can actually 'chat to your DNA': Ask it questions, ponder the impact of mutations, or ask it to generate a small sequence. We'll soon see the frontier labs move away from math and cyber towards bio. It already started, and we believe researchers should have access to capable models. We are releasing the full recipe, so you can do it, too! Model: Blog:

Leandro von Werra

80,179 görüntüleme • 9 gün önce

The trend in China is that more robotics companies are opening stores where anyone can see and interact with the robots. It’s no longer necessary for everyone to contact a salesperson or visit the company’s office. BTW, I like the store designs of both EngineAI and AgiBot!

The trend in China is that more robotics companies are opening stores where anyone can see and interact with the robots. It’s no longer necessary for everyone to contact a salesperson or visit the company’s office. BTW, I like the store designs of both EngineAI and AgiBot!

Tuo Liu

92,466 görüntüleme • 11 ay önce

Hello world A new year has begun and we are living this genocide We are still being killed and living the worst days of our lives in the shadow of genocide We hope that you will stand by us more than ever We hope that this genocide will end as soon as possible Don't forget that Gaza is being bombed now and the planes are above us and we are waiting at any moment to be bombed Share our voice and we will remain part of your day If you can help us through the link we will be grateful to all of you PP🔗 GFM🔗

Hello world A new year has begun and we are living this genocide We are still being killed and living the worst days of our lives in the shadow of genocide We hope that you will stand by us more than ever We hope that this genocide will end as soon as possible Don't forget that Gaza is being bombed now and the planes are above us and we are waiting at any moment to be bombed Share our voice and we will remain part of your day If you can help us through the link we will be grateful to all of you PP🔗 GFM🔗

Abdullah Omar🇵🇸

14,563 görüntüleme • 1 yıl önce

Dreamscale Labs (YC F26) (YC F26) is the first inference provider to serve NVIDIA’s DreamZero robot model over the cloud! And we beat NVIDIA's own reported inference speed by 8%, with just half the silicon. DreamZero is a model that pioneered the new wave of “World Action Models” that has taken over generalist robotics today. It promises to generalize better to new tasks, learn physics from human video, and transfer well to new embodiments. Normally, DreamZero requires 2x GB200 superchips (each costing $60K) or is painfully slow (waits 5.7s before moving 1.6s), which made it infeasible to deploy to robots in practice. Now, with Dreamscale’s real-time cloud inference infrastructure, we’ve demonstrated fast, smooth motion on actual robots with sub-second latency. As scaling laws become more relevant in robotics, frontier physical AI models are growing more computationally expensive than ever. With Dreamscale, we are finally making World Action Models (WAMs) accessible to startups and robot developers across the world. Reach out if you are interested in cloud robot inference.

Dreamscale Labs (YC F26) (YC F26) is the first inference provider to serve NVIDIA’s DreamZero robot model over the cloud! And we beat NVIDIA's own reported inference speed by 8%, with just half the silicon. DreamZero is a model that pioneered the new wave of “World Action Models” that has taken over generalist robotics today. It promises to generalize better to new tasks, learn physics from human video, and transfer well to new embodiments. Normally, DreamZero requires 2x GB200 superchips (each costing $60K) or is painfully slow (waits 5.7s before moving 1.6s), which made it infeasible to deploy to robots in practice. Now, with Dreamscale’s real-time cloud inference infrastructure, we’ve demonstrated fast, smooth motion on actual robots with sub-second latency. As scaling laws become more relevant in robotics, frontier physical AI models are growing more computationally expensive than ever. With Dreamscale, we are finally making World Action Models (WAMs) accessible to startups and robot developers across the world. Reach out if you are interested in cloud robot inference.

Chris Yoo

19,639 görüntüleme • 1 ay önce

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

495,419 görüntüleme • 2 ay önce

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

Over the Reality 🌐

15,316 görüntüleme • 1 ay önce