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Safety in mobile robotics! 👷🏼‍♂️ Robots navigating through crowds is one of the hardest problems in mobile robotics, uncertainty, human motion, and real-time constraints don’t mix well. A team at TU Delft has introduced DRA-MPPI, a new motion-planning method that lets robots move safely through dense pedestrian traffic without...

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Lukas Ziegler

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86,933 次观看 • 9 个月前 •via X (Twitter)

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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 次观看 • 9 个月前

This is not a 2050 warehouse. This is a modern air hub of Amazon. Amazon uses robots to help sort and ship billions of packages each year. At Amazon Air, the company's main air hub in Kentucky, hundreds of self-charging robots zoom across the warehouse floor to organize orders. These robots, called “drives,” follow small stickers on the floor to know where to go and when to speed up or slow down. Amazon has more than ONE MILLION robots in its facilities, and it’s even deploying more new types of robots that can sort items of different shapes and sizes. P.S. Can you guess how many robots they had back in 2013? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:23

This is not a 2050 warehouse. This is a modern air hub of Amazon. Amazon uses robots to help sort and ship billions of packages each year. At Amazon Air, the company's main air hub in Kentucky, hundreds of self-charging robots zoom across the warehouse floor to organize orders. These robots, called “drives,” follow small stickers on the floor to know where to go and when to speed up or slow down. Amazon has more than ONE MILLION robots in its facilities, and it’s even deploying more new types of robots that can sort items of different shapes and sizes. P.S. Can you guess how many robots they had back in 2013? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

181,431 次观看 • 2 个月前

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 might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:47

Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,090 次观看 • 9 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

21,042 次观看 • 7 个月前

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:
0:49

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

273,146 次观看 • 27 天前

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

11,165 次观看 • 3 个月前

A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
3:58

A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

1,806,575 次观看 • 4 个月前

A system with moving transfer plates! 🍽️ Modular transfer plates that move items in any direction. These systems from Festo give factories something really cool; reconfigurable motion that adapts as layouts or processes change. Because they can route items in multiple directions, you can run inspections, branch flows based on sensor data, or perform operations directly on the moving parts. It could even power fully automated storage: shelves that reposition items based on real-time demand. I'd love to see more of them in 3PL centers. Where would you use them? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:25

A system with moving transfer plates! 🍽️ Modular transfer plates that move items in any direction. These systems from Festo give factories something really cool; reconfigurable motion that adapts as layouts or processes change. Because they can route items in multiple directions, you can run inspections, branch flows based on sensor data, or perform operations directly on the moving parts. It could even power fully automated storage: shelves that reposition items based on real-time demand. I'd love to see more of them in 3PL centers. Where would you use them? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

215,443 次观看 • 2 个月前

A system with moving transfer plates! 🍽️ Modular transfer plates that move items in any direction! These systems from Festo give factories something traditional conveyors can’t: fine, reconfigurable motion that adapts as layouts or processes change. Because they can route items in multiple directions, you can run inspections, branch flows based on sensor data, or perform operations directly on the moving parts. It could even power fully automated storage: shelves that reposition items based on real-time demand. I'd love to see more of them in 3PL centers. Where would you use them? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:25

A system with moving transfer plates! 🍽️ Modular transfer plates that move items in any direction! These systems from Festo give factories something traditional conveyors can’t: fine, reconfigurable motion that adapts as layouts or processes change. Because they can route items in multiple directions, you can run inspections, branch flows based on sensor data, or perform operations directly on the moving parts. It could even power fully automated storage: shelves that reposition items based on real-time demand. I'd love to see more of them in 3PL centers. Where would you use them? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

327,650 次观看 • 10 个月前

One tool to handle them all! 🧰 Traditional suction-based systems struggle with over 30% of SKUs. Mesh bags, loose apparel, and porous items often lead to failed picks or damaged goods. Nexera Robotics (Nexera Robotics) is pushing that ceiling to more than 95% by innovating the hardware, not just the software. Their solution? NeuraGrasp®. Instead of rigid fingers or air-dependent suction, NeuraGrasp uses a compliant membrane that conforms to any object. It creates a mechanical lock through friction and wrap-around contact, even on items that leak air or deform. Pressure is distributed by the material itself, meaning no complex force-feedback loops are needed to prevent crushing. It’s single tool can lift heavy payloads, yet pick a single peach without bruising it. A super important part is that no SKU-specific training and no tool changers required. The industry has spent years optimizing robot “brains” while ignoring the hands. Nexera is perfecting the touch to make robots more capable. Congrats Nexera Robotics team! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:59

One tool to handle them all! 🧰 Traditional suction-based systems struggle with over 30% of SKUs. Mesh bags, loose apparel, and porous items often lead to failed picks or damaged goods. Nexera Robotics (Nexera Robotics) is pushing that ceiling to more than 95% by innovating the hardware, not just the software. Their solution? NeuraGrasp®. Instead of rigid fingers or air-dependent suction, NeuraGrasp uses a compliant membrane that conforms to any object. It creates a mechanical lock through friction and wrap-around contact, even on items that leak air or deform. Pressure is distributed by the material itself, meaning no complex force-feedback loops are needed to prevent crushing. It’s single tool can lift heavy payloads, yet pick a single peach without bruising it. A super important part is that no SKU-specific training and no tool changers required. The industry has spent years optimizing robot “brains” while ignoring the hands. Nexera is perfecting the touch to make robots more capable. Congrats Nexera Robotics team! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,230 次观看 • 8 个月前

⚠️ THE ROBOT DIDN’T ENTER A ROBOT TESTING LAB. IT ENTERED A CITY. A humanoid robot is moving through a real urban environment with pedestrians, traffic and constantly changing space around it. There’s no empty runway and no perfectly controlled path - the machine has to keep moving while the world around it does whatever it wants. That makes a few seconds of walking far more interesting than another polished stage demo. A city is one of the hardest places to test a humanoid because nothing stays predictable for long. People stop, cars move, distances change and obstacles appear without warning, while the robot still has to maintain balance and choose where to step next. The challenge isn't simply walking anymore - it's understanding how to move through a world that wasn't designed for robots. And that's the real milestone here. Humanoids don't need to look perfectly human to change the world; they need to become capable of functioning in spaces already built for us. The closer they get to doing that reliably, the less important the laboratory demos become. The future won't feel futuristic because robots look human. It will feel futuristic when robots can move through our world like they belong there.
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⚠️ THE ROBOT DIDN’T ENTER A ROBOT TESTING LAB. IT ENTERED A CITY. A humanoid robot is moving through a real urban environment with pedestrians, traffic and constantly changing space around it. There’s no empty runway and no perfectly controlled path - the machine has to keep moving while the world around it does whatever it wants. That makes a few seconds of walking far more interesting than another polished stage demo. A city is one of the hardest places to test a humanoid because nothing stays predictable for long. People stop, cars move, distances change and obstacles appear without warning, while the robot still has to maintain balance and choose where to step next. The challenge isn't simply walking anymore - it's understanding how to move through a world that wasn't designed for robots. And that's the real milestone here. Humanoids don't need to look perfectly human to change the world; they need to become capable of functioning in spaces already built for us. The closer they get to doing that reliably, the less important the laboratory demos become. The future won't feel futuristic because robots look human. It will feel futuristic when robots can move through our world like they belong there.

Future Memo

10,138 次观看 • 1 个月前

🚨 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 on-device AI. Models run locally, depth is computed locally, and no external PC or cloud pipeline is required. This is why robotics teams love it: lower latency, lower cost, fewer failure points in the field. The hardware is built for the real-world. IP67, shock-resistant, wide-FOV RGB + stereo pair, IR projection, IMU, audio, and a patent-pending calibration system that keeps depth accurate even when conditions change. But the real move is the platform. With Luxonis Hub, you can deploy models, grab telemetry, push OTA updates, or collect data when performance drifts, all from a unified interface. It turns a single device into an end-to-end edge CV system. Most customers today in robotics are groups who just want something that works: AMRs, bin-picking systems, trailer-loading robots, and ag-tech. 🤖 And they all say the same thing, the appeal isn’t raw TOPS, it’s the all-in-one simplicity that lets them scale without building custom infrastructure. Feels like the direction edge vision has been waiting for: rugged hardware + high-throughput on-device compute + a real management layer. A next step toward “plug-and-deploy” perception for robots. 🔗 Find out more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:12

🚨 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 on-device AI. Models run locally, depth is computed locally, and no external PC or cloud pipeline is required. This is why robotics teams love it: lower latency, lower cost, fewer failure points in the field. The hardware is built for the real-world. IP67, shock-resistant, wide-FOV RGB + stereo pair, IR projection, IMU, audio, and a patent-pending calibration system that keeps depth accurate even when conditions change. But the real move is the platform. With Luxonis Hub, you can deploy models, grab telemetry, push OTA updates, or collect data when performance drifts, all from a unified interface. It turns a single device into an end-to-end edge CV system. Most customers today in robotics are groups who just want something that works: AMRs, bin-picking systems, trailer-loading robots, and ag-tech. 🤖 And they all say the same thing, the appeal isn’t raw TOPS, it’s the all-in-one simplicity that lets them scale without building custom infrastructure. Feels like the direction edge vision has been waiting for: rugged hardware + high-throughput on-device compute + a real management layer. A next step toward “plug-and-deploy” perception for robots. 🔗 Find out more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

41,955 次观看 • 9 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

Linear actuators playing football! ⚽️ You can keep scrolling... The "Magic Field" is an impressive display of ball-handling skill, showcasing what fixed automation can achieve. There are no robots at all. Instead of robots, over 300 linear actuators precisely control the ball’s movement on the field, demonstrating incredible accuracy. The setup even includes an advanced kicking mechanism, similar to a SCARA robot, that uses two linear actuators and one rotary actuator to kick the ball into the net. 🥅 This fixed automation shows how powerful and precise automation can be without needing mobile robots on the field. When starting an automation project, it's interesting to consider if fixed automation like this or flexible robots might be the best fit. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:57

Linear actuators playing football! ⚽️ You can keep scrolling... The "Magic Field" is an impressive display of ball-handling skill, showcasing what fixed automation can achieve. There are no robots at all. Instead of robots, over 300 linear actuators precisely control the ball’s movement on the field, demonstrating incredible accuracy. The setup even includes an advanced kicking mechanism, similar to a SCARA robot, that uses two linear actuators and one rotary actuator to kick the ball into the net. 🥅 This fixed automation shows how powerful and precise automation can be without needing mobile robots on the field. When starting an automation project, it's interesting to consider if fixed automation like this or flexible robots might be the best fit. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

235,841 次观看 • 9 个月前

Robots that climb skyscrapers to clean and inspect them! 🧼 Verobotics has built autonomous robots that clean and scan building exteriors, allowing building owners to proactively maintain and upkeep their buildings faster, cheaper, and smarter. The problem is decades old. The only way to clean or inspect a building's exterior has been to lower a person from a BMU (Building Maintenance Unit) at the top of the building. BMUs are heavy, can be out of service, complicated to operate, and limit cleaning to a couple of times per year. Their robots are lightweight, portable, and need no equipment on the roof. One person can deploy multiple robots to the building's facades and let the robots do the rest. Once they're done, the building owner not only has a clean building but also a digital twin of the building exterior for documenting health and identifying issues before they become risks. Robot spots a crack? Noted! They're equipped with onboard cameras that scan the surface, detect window frames to crawl over, and ensure not a spot is missed. That means a consistent result and thorough inspection every time. Onboard sensors continuously detect motion, location, and the surface beneath and around it. The data feeds to a hub that analyzes the surface in real time, building datasets that become the backbone of a digital twin for predictive maintenance. Love seeing the specialized robots bringing value from day 0! :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:39

Robots that climb skyscrapers to clean and inspect them! 🧼 Verobotics has built autonomous robots that clean and scan building exteriors, allowing building owners to proactively maintain and upkeep their buildings faster, cheaper, and smarter. The problem is decades old. The only way to clean or inspect a building's exterior has been to lower a person from a BMU (Building Maintenance Unit) at the top of the building. BMUs are heavy, can be out of service, complicated to operate, and limit cleaning to a couple of times per year. Their robots are lightweight, portable, and need no equipment on the roof. One person can deploy multiple robots to the building's facades and let the robots do the rest. Once they're done, the building owner not only has a clean building but also a digital twin of the building exterior for documenting health and identifying issues before they become risks. Robot spots a crack? Noted! They're equipped with onboard cameras that scan the surface, detect window frames to crawl over, and ensure not a spot is missed. That means a consistent result and thorough inspection every time. Onboard sensors continuously detect motion, location, and the surface beneath and around it. The data feeds to a hub that analyzes the surface in real time, building datasets that become the backbone of a digital twin for predictive maintenance. Love seeing the specialized robots bringing value from day 0! :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

17,616 次观看 • 20 天前

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:
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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 次观看 • 11 个月前

This research is closing a loop! 📿 Literally. Grasping always had a tradeoff: either you’re versatile when forming a grasp, or strong and gentle when holding it, rarely both. This new concept developed at Massachusetts Institute of Technology, called loop closure grasping, sidesteps that tradeoff entirely. During grasp creation, the robot uses an open-loop topology, allowing free, unconstrained motion to wrap around objects of almost any shape. Once the grasp is formed, the mechanism closes the loop, switching into a structure that can hold heavy loads gently, with extremely high compliance and low contact pressure. By changing topology instead of forcing one morphology to do everything, the system achieves something grasping that is strong, gentle, and versatile at the same time. It opens the door to handling objects that have always been hard for robots. Heavy but fragile items, irregular shapes, and even human bodie (that’s how you wake me up on Sunday morning) Congrats Kentaro Barhydt and rest of the involved! 🔥 Here's the paper: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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This research is closing a loop! 📿 Literally. Grasping always had a tradeoff: either you’re versatile when forming a grasp, or strong and gentle when holding it, rarely both. This new concept developed at Massachusetts Institute of Technology, called loop closure grasping, sidesteps that tradeoff entirely. During grasp creation, the robot uses an open-loop topology, allowing free, unconstrained motion to wrap around objects of almost any shape. Once the grasp is formed, the mechanism closes the loop, switching into a structure that can hold heavy loads gently, with extremely high compliance and low contact pressure. By changing topology instead of forcing one morphology to do everything, the system achieves something grasping that is strong, gentle, and versatile at the same time. It opens the door to handling objects that have always been hard for robots. Heavy but fragile items, irregular shapes, and even human bodie (that’s how you wake me up on Sunday morning) Congrats Kentaro Barhydt and rest of the involved! 🔥 Here's the paper: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

17,570 次观看 • 1 个月前

Robots cleaning your cars! 🧼 The Autowash Robotics Operating System went into commercial operation this month at their Lakeside site in Wheat Ridge, after around 14 months of development. Here's what happens when you pull in. The system scans the car in 3D first, reading shape, dimensions, contours and whatever is attached to it. Cargo boxes, spoilers, aftermarket accessories. That becomes a digital model of the vehicle. Two industrial arms then generate a cleaning path for that specific car in real time, rather than running the same fixed spray pattern over everything. Still fully touch-free, with high-pressure filtered water and non-corrosive, PFAS-free chemistry. → Under 20 gallons of water per wash, roughly 76 litres → Clean and dry in about four minutes → A four-bay site draws 320 amps, around half a conventional touch-free installation P.S. Curious how this holds up at scale. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Robots cleaning your cars! 🧼 The Autowash Robotics Operating System went into commercial operation this month at their Lakeside site in Wheat Ridge, after around 14 months of development. Here's what happens when you pull in. The system scans the car in 3D first, reading shape, dimensions, contours and whatever is attached to it. Cargo boxes, spoilers, aftermarket accessories. That becomes a digital model of the vehicle. Two industrial arms then generate a cleaning path for that specific car in real time, rather than running the same fixed spray pattern over everything. Still fully touch-free, with high-pressure filtered water and non-corrosive, PFAS-free chemistry. → Under 20 gallons of water per wash, roughly 76 litres → Clean and dry in about four minutes → A four-bay site draws 320 amps, around half a conventional touch-free installation P.S. Curious how this holds up at scale. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

41,663 次观看 • 22 天前