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Simulation-first approach to robotic production lines! 👾 At Hannover Messe, Amazon Web Services (AWS) and SoftServe are demonstrating a fully autonomous, AI-powered manufacturing workflow. Design a product using generative AI, then watch it move through the entire process: engraving, inspection, and delivery, handled by a mix of robots working...

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Lukas Ziegler

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17,709 次观看 • 5 个月前 •via X (Twitter)

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Humanoid robots are starting to build humanoid robots. UBTECH just put a 14,000㎡ humanoid robot factory into operation in Liuzhou, China, with a designed takt time of one humanoid every 10 minutes and planned annual capacity in the tens of thousands. What’s interesting is that humanoid robots are already part of the production process. Cruzr Y1 and Y2 handle depalletizing, palletizing, material feeding and transport, using 3D vision to adapt to shifted boxes and changing pallet patterns. On the assembly side, Walker S2 and Cruzr are produced on the same line alongside cobots, autonomous logistics vehicles and assistive manipulators. The factory also has a 65㎡ automated warehouse capable of storing 112 humanoids, with AGVs, industrial robots and stacker cranes coordinated as one system. Before production, the factory was modeled 1:1 in Siemens Plant Simulation, covering workstations, racks and AGVs. Each robot gets its own SN, with components, batches, assembly parameters and inspection data tracked all the way to delivery. Humanoid robots are now entering automated manufacturing at the 10,000-unit scale. The next milestone may be when they can take part in enough of the assembly, testing and production process to truly build robots themselves.
1:02

Humanoid robots are starting to build humanoid robots. UBTECH just put a 14,000㎡ humanoid robot factory into operation in Liuzhou, China, with a designed takt time of one humanoid every 10 minutes and planned annual capacity in the tens of thousands. What’s interesting is that humanoid robots are already part of the production process. Cruzr Y1 and Y2 handle depalletizing, palletizing, material feeding and transport, using 3D vision to adapt to shifted boxes and changing pallet patterns. On the assembly side, Walker S2 and Cruzr are produced on the same line alongside cobots, autonomous logistics vehicles and assistive manipulators. The factory also has a 65㎡ automated warehouse capable of storing 112 humanoids, with AGVs, industrial robots and stacker cranes coordinated as one system. Before production, the factory was modeled 1:1 in Siemens Plant Simulation, covering workstations, racks and AGVs. Each robot gets its own SN, with components, batches, assembly parameters and inspection data tracked all the way to delivery. Humanoid robots are now entering automated manufacturing at the 10,000-unit scale. The next milestone may be when they can take part in enough of the assembly, testing and production process to truly build robots themselves.

CyberRobo

17,883 次观看 • 14 天前

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:48

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

22,482 次观看 • 6 个月前

People who've never set foot in a factory will never understand... I watched this three times. For decades, robotics simulation has promised faster deployment. But factories still had to build the real cell to see if it actually worked. Which meant expensive physical prototypes, weeks or months!!! of commissioning, constant surprises between simulation and reality That “sim-to-real gap” has quietly been one of the biggest bottlenecks in manufacturing automation. And it’s exactly what is changing. Today, ABB Robotics announced a partnership with NVIDIA Robotics aimed at closing this gap through the new RobotStudio HyperReality platform: Simulation and real robot behavior can match with near-perfect accuracy. That means manufacturers can design, test, and validate entire production lines before a single robot is installed on the factory floor. The implications are massive: • up to 80% faster setup and commissioning • roughly 40% lower costs by removing physical prototypes • about 50% faster time-to-market for new production lines In other words: Factories can move from trial-and-error engineering to software-driven manufacturing design. Production lines become something you build and validate digitally first. Then deploy physically once everything already works. For an industry that still measures deployment timelines in months or years, this is a major shift. It changes how automation projects are planned, how factories are designed, and how fast manufacturing can adapt to new products. Physical AI actually becomes deployable at an industrial scale. I’ll be at GTC in San Jose next week to see and talk to manufacturers and robotics engineers. If you are into manufacturing like I am, hit me up; my DMs are open!
0:31

People who've never set foot in a factory will never understand... I watched this three times. For decades, robotics simulation has promised faster deployment. But factories still had to build the real cell to see if it actually worked. Which meant expensive physical prototypes, weeks or months!!! of commissioning, constant surprises between simulation and reality That “sim-to-real gap” has quietly been one of the biggest bottlenecks in manufacturing automation. And it’s exactly what is changing. Today, ABB Robotics announced a partnership with NVIDIA Robotics aimed at closing this gap through the new RobotStudio HyperReality platform: Simulation and real robot behavior can match with near-perfect accuracy. That means manufacturers can design, test, and validate entire production lines before a single robot is installed on the factory floor. The implications are massive: • up to 80% faster setup and commissioning • roughly 40% lower costs by removing physical prototypes • about 50% faster time-to-market for new production lines In other words: Factories can move from trial-and-error engineering to software-driven manufacturing design. Production lines become something you build and validate digitally first. Then deploy physically once everything already works. For an industry that still measures deployment timelines in months or years, this is a major shift. It changes how automation projects are planned, how factories are designed, and how fast manufacturing can adapt to new products. Physical AI actually becomes deployable at an industrial scale. I’ll be at GTC in San Jose next week to see and talk to manufacturers and robotics engineers. If you are into manufacturing like I am, hit me up; my DMs are open!

Ilir Aliu

69,120 次观看 • 6 个月前

🚨 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.
1:31

🚨 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 次观看 • 1 年前

Robots moving railcars! 🚃 Did you know that KUKA is also in the mobile robots business? Yes, you heard that right. And not just any robots. Robots that carry up to 56 tons! 🏋🏼 The railcar production process is highly complex, particularly when moving massive cars sideways within a plant. This challenge has been addressed at the Siemens facility in Germany using the OmniMove system from KUKA. The system allows for precise positioning, enabling the units to lift, move, and lower railcars with remarkable accuracy. With Mecanum wheels, the OmniMove units can move seamlessly in any direction, making the process incredibly efficient. Together, these units are capable of lifting up to 56 tons, streamlining heavy railcar production. This one is clearly tele-operated, but just looking at it makes me feel safer when I see a human in the loop. What a great use case for mobile robots! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:38

Robots moving railcars! 🚃 Did you know that KUKA is also in the mobile robots business? Yes, you heard that right. And not just any robots. Robots that carry up to 56 tons! 🏋🏼 The railcar production process is highly complex, particularly when moving massive cars sideways within a plant. This challenge has been addressed at the Siemens facility in Germany using the OmniMove system from KUKA. The system allows for precise positioning, enabling the units to lift, move, and lower railcars with remarkable accuracy. With Mecanum wheels, the OmniMove units can move seamlessly in any direction, making the process incredibly efficient. Together, these units are capable of lifting up to 56 tons, streamlining heavy railcar production. This one is clearly tele-operated, but just looking at it makes me feel safer when I see a human in the loop. What a great use case for mobile robots! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

19,285 次观看 • 2 个月前

Robots from Humanoid passed real factory test at Siemens! 😮‍💨 Humanoid from the UK just completed a successful proof of concept with Siemens, deploying their HMND 01 wheeled humanoid robot in a real working factory. The task was simple but important: pick totes from a storage stack, transport them to a conveyor, and place them at the pickup point for human operators. Repeat until the stack is empty. The robot achieved 60 tote moves per hour, handled two different tote sizes, ran autonomously for more than 30 minutes at a time, stayed operational for over eight hours, and maintained over 90% success rate on pick and place tasks. This matters because it's not a demo video. It's a real deployment in Siemens' Electronics Factory in Erlangen, handling actual production work, measured against real operational metrics. The test ran in two phases. First, Humanoid built a physical twin in-house to test and optimize the system. Then they deployed on-site at Siemens for two weeks, where the robot operated in the real production environment. We can see a wheeled humanoid robot in action! So, now the ultimate question: wheels or legs? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

Robots from Humanoid passed real factory test at Siemens! 😮‍💨 Humanoid from the UK just completed a successful proof of concept with Siemens, deploying their HMND 01 wheeled humanoid robot in a real working factory. The task was simple but important: pick totes from a storage stack, transport them to a conveyor, and place them at the pickup point for human operators. Repeat until the stack is empty. The robot achieved 60 tote moves per hour, handled two different tote sizes, ran autonomously for more than 30 minutes at a time, stayed operational for over eight hours, and maintained over 90% success rate on pick and place tasks. This matters because it's not a demo video. It's a real deployment in Siemens' Electronics Factory in Erlangen, handling actual production work, measured against real operational metrics. The test ran in two phases. First, Humanoid built a physical twin in-house to test and optimize the system. Then they deployed on-site at Siemens for two weeks, where the robot operated in the real production environment. We can see a wheeled humanoid robot in action! So, now the ultimate question: wheels or legs? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

21,993 次观看 • 7 个月前

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:41

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

45,883 次观看 • 3 个月前

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.
2:10

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.

Santiago

22,104 次观看 • 1 年前

Announcing Innovative Dreams, a new production company by Luma and Wonder Project built by filmmakers for filmmakers, and our first production — Moses starring Sir Ben Kingsley, coming this Spring on Prime Video. AI is a fundamental change to the craft and business of filmmaking, and through ID, we are building a new production process for this era — one that brings Humans and AI together to enable great storytelling. We call it Hybrid Production — actors, artists, and generative AI working together in Luma. In real time. Innovative Dreams is a production services company where seasoned filmmakers from Director Jon Erwin's team and Luma's creative technologists work with great studios and filmmakers to help them realize ambitious ideas. It's a liberating force. Hybrid Production enables actors, directors, and artists to work together in real time. Creative teams collaborate with Luma Agents live and make changes to sets, props, lighting, and bring in performance capture from human actors. This is a significant improvement over the current virtual production and performance capture processes where things come together only in post. This is the leverage of AI — not just faster or cheaper, but better than what came before. We have built the first Hybrid Filmmaking studio at Manhattan Beach Studios where Moses is being shot. ID is significantly oversubscribed with demand and is actively growing the team to extend its services to more studios and filmmakers. Read more:
6:02

Announcing Innovative Dreams, a new production company by Luma and Wonder Project built by filmmakers for filmmakers, and our first production — Moses starring Sir Ben Kingsley, coming this Spring on Prime Video. AI is a fundamental change to the craft and business of filmmaking, and through ID, we are building a new production process for this era — one that brings Humans and AI together to enable great storytelling. We call it Hybrid Production — actors, artists, and generative AI working together in Luma. In real time. Innovative Dreams is a production services company where seasoned filmmakers from Director Jon Erwin's team and Luma's creative technologists work with great studios and filmmakers to help them realize ambitious ideas. It's a liberating force. Hybrid Production enables actors, directors, and artists to work together in real time. Creative teams collaborate with Luma Agents live and make changes to sets, props, lighting, and bring in performance capture from human actors. This is a significant improvement over the current virtual production and performance capture processes where things come together only in post. This is the leverage of AI — not just faster or cheaper, but better than what came before. We have built the first Hybrid Filmmaking studio at Manhattan Beach Studios where Moses is being shot. ID is significantly oversubscribed with demand and is actively growing the team to extend its services to more studios and filmmakers. Read more:

Luma

96,065 次观看 • 5 个月前

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:46

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

57,061 次观看 • 4 个月前

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:56

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,898 次观看 • 7 个月前

Elon Musk: “Hold on to your Tesla stock.” Because what’s coming isn’t just another car update—it’s an entirely new paradigm. From Optimus humanoid robots that could one day take care of your kids, walk your dog, and support elderly parents, to CyberCab scaling into mass production, to the Tesla Semi reshaping logistics—this isn’t one product, it’s a full ecosystem. Tesla is simultaneously building AI-powered humanoid labor, autonomous transport at scale, next-gen energy infrastructure through battery, lithium, and cathode production, and expanding global manufacturing across the US and Europe, with five factories ramping major production lines this year alone. And while most companies are focused on the next quarter, Musk is already talking about factories on the moon within 20 years.
2:55

Elon Musk: “Hold on to your Tesla stock.” Because what’s coming isn’t just another car update—it’s an entirely new paradigm. From Optimus humanoid robots that could one day take care of your kids, walk your dog, and support elderly parents, to CyberCab scaling into mass production, to the Tesla Semi reshaping logistics—this isn’t one product, it’s a full ecosystem. Tesla is simultaneously building AI-powered humanoid labor, autonomous transport at scale, next-gen energy infrastructure through battery, lithium, and cathode production, and expanding global manufacturing across the US and Europe, with five factories ramping major production lines this year alone. And while most companies are focused on the next quarter, Musk is already talking about factories on the moon within 20 years.

Ian Miles

223,761 次观看 • 6 个月前

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n
3:26

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

Zhou Xian

3,822,177 次观看 • 1 年前

JUST IN: Unitree Robotics just unveiled the GD01, a manned "mecha" robot that transforms between two legs and four legs in real time. This is not a concept. This is a product. Starting price: $574,000. This robot weighs 500kg, is built from high-strength alloy, and has a torso-mounted cockpit. The demo video shows it walking upright, knocking down a brick wall with its hand, then reconfiguring into a four-legged crawl. Unitree calls it the world's first mass-produced transformable mecha. But the real story here is the bigger picture. Chinese companies accounted for nearly 90% of global humanoid robot sales in 2025. Unitree alone shipped 5,500+ humanoid robots last year. In the same period, Tesla, Figure AI and Agility Robotics each shipped around 150. And Unitree is now filing for an IPO on Shanghai's Star Market, targeting 4.2 billion yuan, 85% of which goes straight into R&D. The West is building incredible humanoid robots. China is building them faster, cheaper and at a scale nobody else is close to matching. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:06

JUST IN: Unitree Robotics just unveiled the GD01, a manned "mecha" robot that transforms between two legs and four legs in real time. This is not a concept. This is a product. Starting price: $574,000. This robot weighs 500kg, is built from high-strength alloy, and has a torso-mounted cockpit. The demo video shows it walking upright, knocking down a brick wall with its hand, then reconfiguring into a four-legged crawl. Unitree calls it the world's first mass-produced transformable mecha. But the real story here is the bigger picture. Chinese companies accounted for nearly 90% of global humanoid robot sales in 2025. Unitree alone shipped 5,500+ humanoid robots last year. In the same period, Tesla, Figure AI and Agility Robotics each shipped around 150. And Unitree is now filing for an IPO on Shanghai's Star Market, targeting 4.2 billion yuan, 85% of which goes straight into R&D. The West is building incredible humanoid robots. China is building them faster, cheaper and at a scale nobody else is close to matching. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

11,690 次观看 • 4 个月前

China is leading in the humanoid race. 🇨🇳 UBTECH Robotics has officially shipped hundreds of its Walker S2 humanoids, not to demos or showcases, but directly into factories and logistics centers. Of course, as is often the case in our robotic world, there were doubts and accusations that it was CGI. This is one of the first large-scale industrial rollouts anywhere in the world. The robots are going to partners like BYD, Geely Auto, FAW-Volkswagen, Dongfeng Liuzhou, and Foxconn, where they’ll be used in frontline production tasks. Let's look at monehh: so UBTECH’s 2025 order book now exceeds 800M yuan (~$113M), and the company targets 500 units delivered by year-end. The Walker S2 is built for continuous operation, including an autonomous battery-swap system that lets it replace its own power pack and keep working without downtime. While Europe is still debating standards and long-term frameworks, China is already doing full production, testing, and integration at scale. 🇪🇺 In the meantime, USA claims that Chinese robots are fake, and China casually keep shipping them. 🇺🇸 The challenge is no longer whether humanoids will be used in industry, it’s who gets them deployed, reliable, and affordable first. And right now, Asia is the one shipping robots by the truckload. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:52

China is leading in the humanoid race. 🇨🇳 UBTECH Robotics has officially shipped hundreds of its Walker S2 humanoids, not to demos or showcases, but directly into factories and logistics centers. Of course, as is often the case in our robotic world, there were doubts and accusations that it was CGI. This is one of the first large-scale industrial rollouts anywhere in the world. The robots are going to partners like BYD, Geely Auto, FAW-Volkswagen, Dongfeng Liuzhou, and Foxconn, where they’ll be used in frontline production tasks. Let's look at monehh: so UBTECH’s 2025 order book now exceeds 800M yuan (~$113M), and the company targets 500 units delivered by year-end. The Walker S2 is built for continuous operation, including an autonomous battery-swap system that lets it replace its own power pack and keep working without downtime. While Europe is still debating standards and long-term frameworks, China is already doing full production, testing, and integration at scale. 🇪🇺 In the meantime, USA claims that Chinese robots are fake, and China casually keep shipping them. 🇺🇸 The challenge is no longer whether humanoids will be used in industry, it’s who gets them deployed, reliable, and affordable first. And right now, Asia is the one shipping robots by the truckload. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

13,890 次观看 • 10 个月前

THIS IS F**KING INSANE THIS GUY MADE $10,188.76 WITH AI WITHOUT GETTING BETTER AT EDITING. He actually removed himself from the editing process entirely. Here’s the workflow: → Find a viral video → Remove the original captions → Feed it into Claude → Claude analyzes the structure and creates a fresh script → AI generates the visuals, voiceover, and captions → Publish Viral content → Analyze → Rewrite → Generate → Caption → Publish → Repeat. The interesting part isn’t the individual tools. It’s the system behind them. Most people ask: “How can AI help me edit faster?” That’s useful, but you’re still doing the work. A better question is: “How much of the entire production pipeline can I remove myself from?” That’s the difference between using AI as a productivity tool and building an AI-powered content business. Saving 30 minutes per video is optimization. Building a pipeline that can produce hundreds of videos without you touching the timeline? That’s a business.
0:39

THIS IS F**KING INSANE THIS GUY MADE $10,188.76 WITH AI WITHOUT GETTING BETTER AT EDITING. He actually removed himself from the editing process entirely. Here’s the workflow: → Find a viral video → Remove the original captions → Feed it into Claude → Claude analyzes the structure and creates a fresh script → AI generates the visuals, voiceover, and captions → Publish Viral content → Analyze → Rewrite → Generate → Caption → Publish → Repeat. The interesting part isn’t the individual tools. It’s the system behind them. Most people ask: “How can AI help me edit faster?” That’s useful, but you’re still doing the work. A better question is: “How much of the entire production pipeline can I remove myself from?” That’s the difference between using AI as a productivity tool and building an AI-powered content business. Saving 30 minutes per video is optimization. Building a pipeline that can produce hundreds of videos without you touching the timeline? That’s a business.

S✧ᜰ

42,289 次观看 • 1 个月前

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.
4:44

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

Melvin

34,306 次观看 • 2 个月前

BBREAKING: A German robotics startup from Stuttgart just gave robots imagination: Production robotics system where robots evaluate the long-term consequences of their actions before executing them in live industrial environments. Until now, every production robot optimised actions locally; reacting to what it sees right now. The problem? Small errors early in a sequence compound over time. A slightly off pick leads to a jam three steps later. A marginal placement leads to a collision five steps after that. Cortex 2.0 from Sereact introduces decision-grounded world models directly into live operations. The system evaluates alternative action sequences, predicts how risk accumulates, and estimates the likelihood of entering unrecoverable states, before the robot commits. The world model is trained exclusively on real-world execution data. No synthetic simulation. No approximate environment models. Learned from how robots actually fail, recover, and succeed in production. Works across form factors, pick-and-place arms, dual-arm systems, and humanoids. One intelligence layer, any robot body. The Stuttgart-based company Sereact raised a €25M Series A led by Creandum last year, backed by Air Street Capital Capital, Point Nine 🇺🇦 and angels including Nico Rosberg. They already run some of the most productive AI-driven robotic systems in live warehouse environments, with customers including Daimler Truck AG and Bol. ... Stuttgart, not San Francisco. 🇩🇪 Kudos to Ralf Gulde and team! Credit:
0:39

BBREAKING: A German robotics startup from Stuttgart just gave robots imagination: Production robotics system where robots evaluate the long-term consequences of their actions before executing them in live industrial environments. Until now, every production robot optimised actions locally; reacting to what it sees right now. The problem? Small errors early in a sequence compound over time. A slightly off pick leads to a jam three steps later. A marginal placement leads to a collision five steps after that. Cortex 2.0 from Sereact introduces decision-grounded world models directly into live operations. The system evaluates alternative action sequences, predicts how risk accumulates, and estimates the likelihood of entering unrecoverable states, before the robot commits. The world model is trained exclusively on real-world execution data. No synthetic simulation. No approximate environment models. Learned from how robots actually fail, recover, and succeed in production. Works across form factors, pick-and-place arms, dual-arm systems, and humanoids. One intelligence layer, any robot body. The Stuttgart-based company Sereact raised a €25M Series A led by Creandum last year, backed by Air Street Capital Capital, Point Nine 🇺🇦 and angels including Nico Rosberg. They already run some of the most productive AI-driven robotic systems in live warehouse environments, with customers including Daimler Truck AG and Bol. ... Stuttgart, not San Francisco. 🇩🇪 Kudos to Ralf Gulde and team! Credit:

Ilir Aliu

72,419 次观看 • 7 个月前