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New Gemini Robotics 1.5 models will enable robots to better reason, plan ahead, use digital tools like Search, and transfer learning from one kind of robot to another. Our next big step towards general-purpose robots that are truly helpful — you can see how the robot reasons as it... show more

Sundar Pichai

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496,266 views • 9 months ago •via X (Twitter)

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RoboPapers

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Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

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Mahi Shafiullah 🏠🤖

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Robotics is entering the mainstream! 👀 Let's accept this fact. Consumptionism – stonks. Possibilities (HW + SW) – stonks. Demographics - not stonks. Modern robots are often designed to interact with our legacy world, whether it's a household, a factory, or a shipyard. That means they often need to work with existing machines, tools, and spaces. In short: retrofitting. Improving old machines with new technologies without major interference in their operation. Modern way of integrating things. You don't have to change the entire environment to deploy a robot. The robot will adapt, just as human would when freshly hired. The promise is to have an army of robots to help with the world designed for us. I also believe that general purpose robots, are not a direct competition for single purpose machines (like a highly efficient specialized packaging machine, for example). It's like adding a robot to this carriage. Specialized machine could be a car ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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14,396 views • 5 months ago

today we are releasing Le Lamp. an open source expressive robot. expressive interaction design is the missing layer in robotics. the future isn’t just functional robots - it’s robots that feel natural to use and are designed from the ground up for expressive human-robot interaction. we will continue iterating Le Lamp to make it more expressive and useful in the upcoming weeks. github: discord: order the Le Lamp kit:

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57,565 views • 11 months ago

Our robots now run autonomously >80% of the time. We still have human teleoperators in-the-loop, who take control when the robot gets stuck or something goes wrong. As autonomy improves, we'll grow our robot-to-operator efficiency until we have one person overseeing 10+ robots. Useful, AI-powered robots are here. And it's only going to get better.

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Are you ready for the Bad Mad Robot invasion? 🤖 We tested the closed beta with many legends. BADMAD ROBOTS is pushing the challenge! It was my pleasure to be there. You can see how you can get many rewards such as Closed Beta, Ambassador Program and ROBOTS BOX in the article below 👇

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TSUBASA 🥷🎮

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A cute humanoid robot from NYC Fauna Robotics has released a humanoid robot called Sprout, which is approximately 1 meter tall, lightweight, quiet, and features no dangerous edges and soft skin. Its appearance and interactions are designed to be "approachable," like a social companion. It is currently offered as a general-purpose robotics platform for developers, providing basic movement, perception, navigation, and expression capabilities, allowing real-world use cases to emerge within the community. Many humanoid robots are born in factories and warehouses, but Fauna Robotics aims to bring humanoid robots back into human living spaces. Fauna believes that if humanoid robots are to become as ubiquitous as computers and mobile phones in the future, the starting point must be a safe, friendly form suitable for homes, schools, and offices. Sprout is their first step in this direction.

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Tiff In Tech (600K+ on YouTube) just dropped a new video: Robots Are ‘Doomed to Succeed’ and That’s a Problem. Excited to see OpenMind featured in the conversation, highlighting the missing gaps in physical AI we solve: - Robot understanding of human intent - Building real social intelligence in robots - Operating systems that work across all robot manufacturers and form-factors Check out the full video below👇

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.Arielle "AZ" Zuckerberg on the new frontier of robotics: value shifts as training costs collapse. “The cost of training robots to do something that’s actually creating value has just gone down immensely.” “I agree that having a robot in my house seems a little Black Mirror-esque. But people are receptive to personal relationships with AI, like ChatGPT as a friend and coach.” “I’m much more into the idea of ambient, single-purpose objects. For instance, a robot that lives in the laundry room, moving and folding your laundry.”

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A pentagon robot! 🖐🏽 Compared to typical SCARA models, it delivers higher speed, improved stability, better space efficiency, and can operate smoothly in tight areas, even through singularity positions. These robots are a good fit for handling complex tasks in challenging environments where conventional robots often fall short. Have you ever seen a robot like this? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

A pentagon robot! 🖐🏽 Compared to typical SCARA models, it delivers higher speed, improved stability, better space efficiency, and can operate smoothly in tight areas, even through singularity positions. These robots are a good fit for handling complex tasks in challenging environments where conventional robots often fall short. Have you ever seen a robot like this? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

42,565 views • 3 months ago

The robot is learning several novel tasks instantly, after just ONE demonstration each... Instant Policy makes it possible: no extra training, no weight updates, just pure in-context learning. It just got accepted at ICLR 2025, and it’s changing how robots learn. With just a single demo, a robot can pick up a new task and start performing it right away. Why this is a big deal: ✅ Learns tasks instantly with just one or a few demonstrations ✅ Improves over time as more demonstrations are given ✅ Uses simulation-based training with “pseudo-demonstrations” for scalability ✅ Can transfer skills across different robots and even follow language-defined tasks It brings in-context learning to robotics, opening up new possibilities for flexible, real-world automation. You can try it yourself: code and weights are available at • • • • Thank you to Edward Johns, Director of the Robot Learning Lab at Imperial College for sharing their work! 🙏

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Ilir Aliu

46,285 views • 1 year ago

Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Shruti

58,755 views • 25 days ago

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

Patrick OShaughnessy

133,833 views • 3 months ago

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,472 views • 1 year ago

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Don't sleep on humanoid robots. Humanoid robots are going to play a profound role in our lives, and it will likely be sooner than most expect. I discuss some pivotal questions with jimmah in my effort to gain insight and clarity: - How do you evaluate Tesla's (and other companies') progress with humanoid robots? - Who will be the winner? Or will there be multiple big winners? - How long will it take until we see millions of humanoid robots produced annually? - What's harder for humanoid robots - the hardware of software? - How can we track progress across humanoid robot companies? - What's the next big milestones or demonstrations Tesla can show? If you're going to spend an hour listening/watching anything online this week, I highly recommend this!

Dave Lee

53,757 views • 1 year ago