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Memo is a robot that uses AI to perform household tasks effectively. Today Sunday announced its Series B, and we’re proud to be investors. Training robots for the home is hard — the environment is messy, dynamic, and full of edge cases. So Sunday is training robots directly on... show more

Bain Capital Ventures

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21,996 次观看 • 5 个月前 •via X (Twitter)

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Elon Musk today on Optimus robot: "You’ve probably seen a lot of impressive demos of robots on the internet, but those demonstrations are pre-programmed or remote-controlled. There is no humanoid robot that can actually do generalized tasks. Optimus will be the first one that will be capable of doing that, in just a demo, it’s generally useful in day-to-day life."

Elon Musk today on Optimus robot: "You’ve probably seen a lot of impressive demos of robots on the internet, but those demonstrations are pre-programmed or remote-controlled. There is no humanoid robot that can actually do generalized tasks. Optimus will be the first one that will be capable of doing that, in just a demo, it’s generally useful in day-to-day life."

Nic Cruz Patane

120,088 次观看 • 1 个月前

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

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

87,750 次观看 • 2 个月前

Teaching robots how to paint! 💅🏼 This painting robot, for example, mimics human movements with precision to handle repetitive, and tiring tasks. A special device memorizes points in 3D space, which are then sent to the robot's control. The result? A robot that, after a single demonstration, can perform a given action. Keep in mind that it's taught as 'fixed'. 👨🏻‍🔧 That is, it will not be able to react to an anomaly or changing environment. But ultimately, with fewer workers available and many avoiding tough, dirty jobs, robots are helping industries stay efficient and safe. ♻️ RT to help 1 robot find a new workplace!

Teaching robots how to paint! 💅🏼 This painting robot, for example, mimics human movements with precision to handle repetitive, and tiring tasks. A special device memorizes points in 3D space, which are then sent to the robot's control. The result? A robot that, after a single demonstration, can perform a given action. Keep in mind that it's taught as 'fixed'. 👨🏻‍🔧 That is, it will not be able to react to an anomaly or changing environment. But ultimately, with fewer workers available and many avoiding tough, dirty jobs, robots are helping industries stay efficient and safe. ♻️ RT to help 1 robot find a new workplace!

Lukas Ziegler

38,319 次观看 • 1 年前

System ID for legged robots is hard: (1) Discontinuous dynamics and (2) many parameters to identify and hard to "excite" them. SPI-Active is a general tool for legged robot system ID. Key ideas: (1) massively parallel sampling-based optimization, (2) structured parameter space, and (3) active exploration based on Fisher Information to collect the most informative data in real. SPI-Active provides an accurate robot model and effectively reduces the sim2real gap. In sim2real policy learning setting, it outperforms baselines by 42-63% in various quadruped & humanoid tasks. Led by Nikhil Sobanbabu Guanqi He

System ID for legged robots is hard: (1) Discontinuous dynamics and (2) many parameters to identify and hard to "excite" them. SPI-Active is a general tool for legged robot system ID. Key ideas: (1) massively parallel sampling-based optimization, (2) structured parameter space, and (3) active exploration based on Fisher Information to collect the most informative data in real. SPI-Active provides an accurate robot model and effectively reduces the sim2real gap. In sim2real policy learning setting, it outperforms baselines by 42-63% in various quadruped & humanoid tasks. Led by Nikhil Sobanbabu Guanqi He

Guanya Shi

21,442 次观看 • 1 年前

What does it look like when a robot truly understands where it is in the real world? It starts with Niantic Spatial’s Scaniverse, capturing a real-world environment and reconstructing it into a high-fidelity Gaussian splat. From there, our Robot Adventures demo simulates how VPS could localize a robot within that environment, mirroring 360° video of it navigating the physical space. 🔗Get started with Scaniverse today: #NianticSpatial #Scaniverse #Reconstruction #VPS #GeospatialAI #AI #PhysicalAI #Robotics

What does it look like when a robot truly understands where it is in the real world? It starts with Niantic Spatial’s Scaniverse, capturing a real-world environment and reconstructing it into a high-fidelity Gaussian splat. From there, our Robot Adventures demo simulates how VPS could localize a robot within that environment, mirroring 360° video of it navigating the physical space. 🔗Get started with Scaniverse today: #NianticSpatial #Scaniverse #Reconstruction #VPS #GeospatialAI #AI #PhysicalAI #Robotics

Niantic Spatial 🌎

20,350 次观看 • 3 个月前

Researchers at Columbia University have developed modular robots that can adapt, repair, and even rebuild themselves using a concept called robot metabolism. 🤖 Instead of remaining fixed, these robots can detach, reconnect, and reorganize their own structure based on the task or environment. If one part is damaged, the system can replace or rearrange itself rather than stopping completely. This could reshape the future of disaster response, industrial automation, and even space exploration. The idea of robots that evolve instead of wear out is becoming more than science fiction. What real-world application do you think will benefit most from this technology? 🎥 Media: Columbia University ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

Researchers at Columbia University have developed modular robots that can adapt, repair, and even rebuild themselves using a concept called robot metabolism. 🤖 Instead of remaining fixed, these robots can detach, reconnect, and reorganize their own structure based on the task or environment. If one part is damaged, the system can replace or rearrange itself rather than stopping completely. This could reshape the future of disaster response, industrial automation, and even space exploration. The idea of robots that evolve instead of wear out is becoming more than science fiction. What real-world application do you think will benefit most from this technology? 🎥 Media: Columbia University ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

CTO ROBOTICS Media

3,692,074 次观看 • 2 个月前

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 →

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 次观看 • 8 个月前

🚨🇺🇸 ELON: HUMANOID ROBOTS WILL BE BIGGER THAN CELL PHONES, EVERYONE WILL WANT ONE Elon is once again thinking far beyond EVs, declaring that humanoid robots will be “the biggest industry or the biggest product ever,” even surpassing smartphones. Elon is especially referring to Tesla’s Optimus robot, which he claims will eventually be capable of doing anything humans don’t want to. Tesla is already training Optimus with real-world tasks using AI and data from its vehicle fleet, and Musk believes mass production could redefine both labor and consumer tech. Some call it hype, but Elon calls it inevitable. If he’s right, we’re not just talking about the next iPhone… we’re talking about the next industrial revolution. Source: Tesla Owners Silicon Valley

🚨🇺🇸 ELON: HUMANOID ROBOTS WILL BE BIGGER THAN CELL PHONES, EVERYONE WILL WANT ONE Elon is once again thinking far beyond EVs, declaring that humanoid robots will be “the biggest industry or the biggest product ever,” even surpassing smartphones. Elon is especially referring to Tesla’s Optimus robot, which he claims will eventually be capable of doing anything humans don’t want to. Tesla is already training Optimus with real-world tasks using AI and data from its vehicle fleet, and Musk believes mass production could redefine both labor and consumer tech. Some call it hype, but Elon calls it inevitable. If he’s right, we’re not just talking about the next iPhone… we’re talking about the next industrial revolution. Source: Tesla Owners Silicon Valley

Mario Nawfal

354,581 次观看 • 9 个月前

Engineers: “This is a breakthrough in soft robotics.” Twitter: “Bro built an AI vibrator.” 🤦‍♂️ Researchers at the University of Southern Denmark created a soft robot that moves by inflating and contracting its body like a worm. Jokes aside… robots like this could actually be useful for rescue missions and environments where traditional robots fail. But let’s be honest: you already know what most comment sections are going to talk about 💀 🎥 Media: SDU Soft Robotics ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

Engineers: “This is a breakthrough in soft robotics.” Twitter: “Bro built an AI vibrator.” 🤦‍♂️ Researchers at the University of Southern Denmark created a soft robot that moves by inflating and contracting its body like a worm. Jokes aside… robots like this could actually be useful for rescue missions and environments where traditional robots fail. But let’s be honest: you already know what most comment sections are going to talk about 💀 🎥 Media: SDU Soft Robotics ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

CTO ROBOTICS Media

420,453 次观看 • 2 个月前

If you are wondering why Elon Musk has focused all his energy and wealth on producing reusable rockets, AI, and robots… Recently, he announced that they will stop the production of some car models and replace it with robot production. The robots will become their workforce and guards. The bunkers will be underground, but the safest bet during a Geophysical Event is to be in space and return later, once everything settles down.

If you are wondering why Elon Musk has focused all his energy and wealth on producing reusable rockets, AI, and robots… Recently, he announced that they will stop the production of some car models and replace it with robot production. The robots will become their workforce and guards. The bunkers will be underground, but the safest bet during a Geophysical Event is to be in space and return later, once everything settles down.

Open Minded Approach

231,949 次观看 • 6 个月前

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?

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 次观看 • 1 年前

The 2nd World Humanoid Robot Games opened today in Beijing, and a world record has already been set. The Lightning robot, developed by smartphone maker Honor, ran 100 meters in 9.32 seconds, beating Usain Bolt's human record of 9.58 seconds. A total of 2,056 humanoid robots from 666 teams are participating in the games, competing in various real-world tasks and athletic events, such as house cleaning, firefighting, soccer, running, boxing, and more. Comment from me: Artificial intelligence is the foundation of future technological and environmental development. Today, we can see that two global centers have emerged in terms of the number of data centers, AI infrastructure, and the integration of AI into industrial and humanoid robotics: the United States, along with Japan and South Korea, and China. Russia is not an independent center and is lagging far behind the leaders, having squandered all its potential in the war. Europe is currently following the US lead, but it is already developing its own AI structures and semiconductor strategy, changing regulations, and investing hundreds of billions in technological autonomy, and it could become the third global center.

The 2nd World Humanoid Robot Games opened today in Beijing, and a world record has already been set. The Lightning robot, developed by smartphone maker Honor, ran 100 meters in 9.32 seconds, beating Usain Bolt's human record of 9.58 seconds. A total of 2,056 humanoid robots from 666 teams are participating in the games, competing in various real-world tasks and athletic events, such as house cleaning, firefighting, soccer, running, boxing, and more. Comment from me: Artificial intelligence is the foundation of future technological and environmental development. Today, we can see that two global centers have emerged in terms of the number of data centers, AI infrastructure, and the integration of AI into industrial and humanoid robotics: the United States, along with Japan and South Korea, and China. Russia is not an independent center and is lagging far behind the leaders, having squandered all its potential in the war. Europe is currently following the US lead, but it is already developing its own AI structures and semiconductor strategy, changing regulations, and investing hundreds of billions in technological autonomy, and it could become the third global center.

Anton Gerashchenko

24,381 次观看 • 2 天前

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora

23,489 次观看 • 10 个月前

> be me > be europoor > wake up on a Sunday, walk 8 min from the house (that i personally own, not indebted for 35 yrs of mortgage that is one recession away from being repossessed by the bank) to the beach > do yoga > meet your neighbours that are having a picnic > have a glass of wine with them > come back and enjoy peaceful Easter Sunday knowing you have no debt, own all your assets, your government debt-to-GDP is 60% and a health emergency won’t make you homeless. > enjoy the peace of knowing that when the global system implodes, you are well equipped to handle it without your nation turning into a Mad Max apocalypse. Be Europoor 🤌 Happy Sunday to y’all and Христос Воскресе for all those that celebrate.

> be me > be europoor > wake up on a Sunday, walk 8 min from the house (that i personally own, not indebted for 35 yrs of mortgage that is one recession away from being repossessed by the bank) to the beach > do yoga > meet your neighbours that are having a picnic > have a glass of wine with them > come back and enjoy peaceful Easter Sunday knowing you have no debt, own all your assets, your government debt-to-GDP is 60% and a health emergency won’t make you homeless. > enjoy the peace of knowing that when the global system implodes, you are well equipped to handle it without your nation turning into a Mad Max apocalypse. Be Europoor 🤌 Happy Sunday to y’all and Христос Воскресе for all those that celebrate.

Бианка

429,316 次观看 • 4 个月前

Today, we're proud to announce that Syndicate Network is one of the FIRST decentralized networks to be built and launched in AMERICA—formed under a Wyoming-based DUNA with no offshore entities. This is a major step forward for the crypto industry and U.S. 🇺🇸 Learn more ↓

Today, we're proud to announce that Syndicate Network is one of the FIRST decentralized networks to be built and launched in AMERICA—formed under a Wyoming-based DUNA with no offshore entities. This is a major step forward for the crypto industry and U.S. 🇺🇸 Learn more ↓

Syndicate

179,159 次观看 • 1 年前

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 次观看 • 6 个月前

Tomorrow is going to be a historic day (June 12, 2026) The world's most ambitious company is going public: SpaceX For the first time, everyday investors have a chance to own a piece of the company that changed the trajectory of space forever After years of building the future of space travel, reusable rockets, Starlink, and the path to Mars, SpaceX is stepping into the public markets This is a company that has completely redefined what’s possible in space, and now everyday investors can be part of it From Starship to Starlink to the future of humanity's multiplanetary life, SpaceX is the key that unlocks the door Tomorrow marks a new chapter

Tomorrow is going to be a historic day (June 12, 2026) The world's most ambitious company is going public: SpaceX For the first time, everyday investors have a chance to own a piece of the company that changed the trajectory of space forever After years of building the future of space travel, reusable rockets, Starlink, and the path to Mars, SpaceX is stepping into the public markets This is a company that has completely redefined what’s possible in space, and now everyday investors can be part of it From Starship to Starlink to the future of humanity's multiplanetary life, SpaceX is the key that unlocks the door Tomorrow marks a new chapter

X Freeze

62,419 次观看 • 2 个月前