Загрузка видео...

Не удалось загрузить видео

На главную

🚨 BREAKING: Big news in the computer vision world! 🎥 Luxonis | Robotic Vision just dropped its new OAK 4 line, and it’s a big upgrade for edge computer vision. Instead of being “just a stereo camera,” OAK 4 is a fully standalone vision computer with 52 TOPS of...

lukas_m_ziegler's profile picture

Lukas Ziegler

40,890 subscribers

41,955 просмотров • 8 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.
5:26

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 просмотров • 6 месяцев назад

Your phone can build voxel world now! 👾 This is an open-source tool that converts phone video into 3D point clouds. You can run Depth Anything 3 locally. Basically just local GPU inference. The interesting part is how it handles messy input. Video windows overlap, scale gets converted to real meters, drift gets corrected after inference. The system stitches everything together even when footage is noisy. Objects far away might fail strict confidence filters but still show up as recognizable buildings. That's huge for small robots because perception becomes cheap and stays local. Obviously it wouldn't be a phone for robots, but it shows the potential of edge/local perception processing. A robot can understand its environment without offloading to the cloud. Real-world video has artifacts and occlusions. The system doesn't need perfect input to reconstruct scene structure. It works with what's there. For robotics, this means small platforms can build 3D maps autonomously using hardware they already have. Here's the repo: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:33

Your phone can build voxel world now! 👾 This is an open-source tool that converts phone video into 3D point clouds. You can run Depth Anything 3 locally. Basically just local GPU inference. The interesting part is how it handles messy input. Video windows overlap, scale gets converted to real meters, drift gets corrected after inference. The system stitches everything together even when footage is noisy. Objects far away might fail strict confidence filters but still show up as recognizable buildings. That's huge for small robots because perception becomes cheap and stays local. Obviously it wouldn't be a phone for robots, but it shows the potential of edge/local perception processing. A robot can understand its environment without offloading to the cloud. Real-world video has artifacts and occlusions. The system doesn't need perfect input to reconstruct scene structure. It works with what's there. For robotics, this means small platforms can build 3D maps autonomously using hardware they already have. Here's the repo: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,427 просмотров • 1 месяц назад

Trash isn’t just trash anymore! 🗑️ With vision, sensors, and AI, waste becomes a sorting problem, and a real business opportunity. Here's a robotic system from ZenRobotics - A Terex Brand. Their systems now sort everything from bulky construction debris to high-speed conveyor waste with near-perfect precision. The Heavy Picker handles large, awkward materials like stone and scrap. The Fast Picker operates at high speeds, ideal for lighter materials in material recovery facilities. And behind it all is ZenBrain, an AI trained on over 500 waste categories, making real-time decisions without a human in sight. Great to see robots in waste management industry! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:01

Trash isn’t just trash anymore! 🗑️ With vision, sensors, and AI, waste becomes a sorting problem, and a real business opportunity. Here's a robotic system from ZenRobotics - A Terex Brand. Their systems now sort everything from bulky construction debris to high-speed conveyor waste with near-perfect precision. The Heavy Picker handles large, awkward materials like stone and scrap. The Fast Picker operates at high speeds, ideal for lighter materials in material recovery facilities. And behind it all is ZenBrain, an AI trained on over 500 waste categories, making real-time decisions without a human in sight. Great to see robots in waste management industry! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

11,134 просмотров • 1 месяц назад

Something must change in robotics! 😖 As more billion-dollar players enter the space, more systems are closing off. APIs move behind paywalls, platforms lock down, and robots turn into black boxes. Pollen Robotics is taking the opposite route. With Reachy 2, everything is open: the code, the CAD files, the ROS 2 interfaces, the communication stack. If you want to swap the camera, redesign the gripper, or fork the whole system, you can, and that’s the point. It creates a culture where universities can run real experiments without licensing drama, and startups can prototype without waiting for vendor permission. Clone it on GitHub. Add behaviors. Share datasets. Fork the models. Push improvements upstream. The Hugging Face influence is real. It’s two open ecosystems aligning around the same idea: robotics should be built by more than a handful of companies. It was so cool to meet the team behind Reachy 2 in Bordeaux! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:16

Something must change in robotics! 😖 As more billion-dollar players enter the space, more systems are closing off. APIs move behind paywalls, platforms lock down, and robots turn into black boxes. Pollen Robotics is taking the opposite route. With Reachy 2, everything is open: the code, the CAD files, the ROS 2 interfaces, the communication stack. If you want to swap the camera, redesign the gripper, or fork the whole system, you can, and that’s the point. It creates a culture where universities can run real experiments without licensing drama, and startups can prototype without waiting for vendor permission. Clone it on GitHub. Add behaviors. Share datasets. Fork the models. Push improvements upstream. The Hugging Face influence is real. It’s two open ecosystems aligning around the same idea: robotics should be built by more than a handful of companies. It was so cool to meet the team behind Reachy 2 in Bordeaux! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

21,046 просмотров • 8 месяцев назад

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.
2:22

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

Rohan Paul

25,312 просмотров • 1 месяц назад

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

56,807 просмотров • 2 месяцев назад

Magnetic levitation for surgical robots! 🧲 A research team working on minimally invasive surgery just introduced a remote magnetic levitation to move things inside our bodies. Their main goal is to control guidewires and catheters inside the body using magnetic fields. But levitation is unstable and extreme, it forces them to solve fundamental control problems they can't avoid. They have to handle strong magnetic field nonlinearities, develop real-time invertible models, and create control methods that work on any eMNS (electroMagnetic Navigation System) and any object. The real challenge is the position and orientation feedback. You can control a magnet outside the body, but once a catheter or drug-delivery robot is inside a patient, you can't see it. There's no vision, but just magnetic fields and current measurements. That's huge news coming out of ETH Zurich! Here's the paper: Mentioned all authors on Li, but coulnd't find them here! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:20

Magnetic levitation for surgical robots! 🧲 A research team working on minimally invasive surgery just introduced a remote magnetic levitation to move things inside our bodies. Their main goal is to control guidewires and catheters inside the body using magnetic fields. But levitation is unstable and extreme, it forces them to solve fundamental control problems they can't avoid. They have to handle strong magnetic field nonlinearities, develop real-time invertible models, and create control methods that work on any eMNS (electroMagnetic Navigation System) and any object. The real challenge is the position and orientation feedback. You can control a magnet outside the body, but once a catheter or drug-delivery robot is inside a patient, you can't see it. There's no vision, but just magnetic fields and current measurements. That's huge news coming out of ETH Zurich! Here's the paper: Mentioned all authors on Li, but coulnd't find them here! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

120,803 просмотров • 1 месяц назад

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,835 просмотров • 7 месяцев назад

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:20

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,361 просмотров • 2 месяцев назад

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.
1:31:59

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.

Robert Scoble

17,484 просмотров • 3 месяцев назад

Trained a humanoid entirely in a 3D scan of the office. Zero real-world fine-tuning. It just walked in and worked. RL needs hundreds of thousands of attempts, and real robots can't afford to crash. A misjudged gap or a glass door collision breaks hardware and costs hours resetting. So you train in a sim. But sim policies usually train on randomized, untextured geometry; depth is easy to fake. The robot learns structure, not the real world: no materials, no lighting, no idea what anything actually is. RGB cameras carry all of that but training RGB policies in generic fake worlds won’t generalize to the real world. Niantic Spatial 🌎 Scaniverse reconstructs your scan of the real deployment site. One 360° camera walkthrough → photorealistic 3D Gaussian splat at metric scale → collision mesh pulled from the same reconstruction, so vision and physics match exactly. Drops straight into NVIDIA Isaac Sim/Lab, no manual conversion. Flexion simulation-first approach then seamlessly enables the training of RGB-only nav policies inside that reconstruction. With added domain randomization + large image encoders for robustness, this deploys straight to hardware. No real-world fine-tuning. Deployment: months of on-site adaptation → days. Tune into the NVIDIA livestream on 12 August to hear how these companies are closing the sim2real gap: NVIDIA Robotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:47

Trained a humanoid entirely in a 3D scan of the office. Zero real-world fine-tuning. It just walked in and worked. RL needs hundreds of thousands of attempts, and real robots can't afford to crash. A misjudged gap or a glass door collision breaks hardware and costs hours resetting. So you train in a sim. But sim policies usually train on randomized, untextured geometry; depth is easy to fake. The robot learns structure, not the real world: no materials, no lighting, no idea what anything actually is. RGB cameras carry all of that but training RGB policies in generic fake worlds won’t generalize to the real world. Niantic Spatial 🌎 Scaniverse reconstructs your scan of the real deployment site. One 360° camera walkthrough → photorealistic 3D Gaussian splat at metric scale → collision mesh pulled from the same reconstruction, so vision and physics match exactly. Drops straight into NVIDIA Isaac Sim/Lab, no manual conversion. Flexion simulation-first approach then seamlessly enables the training of RGB-only nav policies inside that reconstruction. With added domain randomization + large image encoders for robustness, this deploys straight to hardware. No real-world fine-tuning. Deployment: months of on-site adaptation → days. Tune into the NVIDIA livestream on 12 August to hear how these companies are closing the sim2real gap: NVIDIA Robotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

117,692 просмотров • 4 дней назад

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.
0:31

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 просмотров • 6 месяцев назад

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:
0:29

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 просмотров • 9 месяцев назад

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

19,148 просмотров • 1 месяц назад

What happens when mobile manipulators go on a grid❓ This week Brightpick announced Gridpicker, its new robotic system for high-throughput applications. Combining AI-powered mobile manipulation with a high-density grid design, Gridpicker achieves up to 2x the throughput of shuttle systems at 40% lower cost and using 5x less labor. Most fascinating: The cost of Gridpicker DECREASES as automation increases 🤯 Traditional systems separate storage and picking. If you want more robotic picking, you add expensive robotic cells, more infrastructure, and more complexity. Gridpicker was designed differently: Each robot both retrieves items and picks them. The greater the share of robotically pickable items: The more productive each robot becomes The fewer robots are needed The lower the cost per order That means progress in AI and VLAs directly translate into better system economics over time. Hats off to Jan Zizka that! 👏 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:30

What happens when mobile manipulators go on a grid❓ This week Brightpick announced Gridpicker, its new robotic system for high-throughput applications. Combining AI-powered mobile manipulation with a high-density grid design, Gridpicker achieves up to 2x the throughput of shuttle systems at 40% lower cost and using 5x less labor. Most fascinating: The cost of Gridpicker DECREASES as automation increases 🤯 Traditional systems separate storage and picking. If you want more robotic picking, you add expensive robotic cells, more infrastructure, and more complexity. Gridpicker was designed differently: Each robot both retrieves items and picks them. The greater the share of robotically pickable items: The more productive each robot becomes The fewer robots are needed The lower the cost per order That means progress in AI and VLAs directly translate into better system economics over time. Hats off to Jan Zizka that! 👏 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

29,457 просмотров • 4 месяцев назад

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.
2:48

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

383,230 просмотров • 1 год назад

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:01

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

207,379 просмотров • 6 месяцев назад

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
1:23

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

46,927 просмотров • 8 месяцев назад

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

11,580 просмотров • 3 месяцев назад

Robot trained entirely in simulation! 🤯 Sudo AI just released #sudo R1. This could be the moment the data bottleneck in robotics finally breaks, a major step forward for getting simulation-trained robots to actually work in the real world. #sudo R1 is a manipulation foundation model that reliably picks objects it has never seen: glass, fabric, metal, irregular shapes. All with zero real-world training data. No tele-op. No demos. No manual labeling. Pure simulation. And picking isn't just any skill, it's the foundation. Almost everything we want robots to do starts with being able to reliably pick something up. Data collection is the bottleneck holding the whole field back. Every new task means hours of tele-op and armies of labelers. If simulation alone can actually deliver production-grade picking, that bottleneck starts to disappear. Really exciting to see! If the results hold, the path to general-purpose robots just got a whole lot shorter. 👇 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:59

Robot trained entirely in simulation! 🤯 Sudo AI just released #sudo R1. This could be the moment the data bottleneck in robotics finally breaks, a major step forward for getting simulation-trained robots to actually work in the real world. #sudo R1 is a manipulation foundation model that reliably picks objects it has never seen: glass, fabric, metal, irregular shapes. All with zero real-world training data. No tele-op. No demos. No manual labeling. Pure simulation. And picking isn't just any skill, it's the foundation. Almost everything we want robots to do starts with being able to reliably pick something up. Data collection is the bottleneck holding the whole field back. Every new task means hours of tele-op and armies of labelers. If simulation alone can actually deliver production-grade picking, that bottleneck starts to disappear. Really exciting to see! If the results hold, the path to general-purpose robots just got a whole lot shorter. 👇 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

25,901 просмотров • 3 месяцев назад