Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Why add sensors and complex systems when physics can do the job? This production line sorts products using only weight and controlled bursts of air. ✅ No cameras or vision models ✅ No expensive integration ✅ Just reliable, repeatable separation at scale It’s a reminder that not every automation... show more

Ilir Aliu

53,807 subscribers

532,401 Aufrufe • vor 8 Monaten •via X (Twitter)

Bildung Nachrichten & Politik Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

1,676,561 Aufrufe • vor 9 Monaten

The most interesting part of this robot is not the arm. It is the gripper. Instead of pushing the whole robot faster, this setup adds speed at the end effector. The gripper itself becomes a moving axis that can accelerate independently of the arm. That changes the physics of the system. • Faster cycle times without stressing the robot structure • Less inertia to fight against • Motion where it actually matters, at the tool center point It is a reminder that many performance limits in robotics are not solved by bigger motors, but by smarter mechanics. ——- Weekly robotics and AI insights. Subscribe free:

The most interesting part of this robot is not the arm. It is the gripper. Instead of pushing the whole robot faster, this setup adds speed at the end effector. The gripper itself becomes a moving axis that can accelerate independently of the arm. That changes the physics of the system. • Faster cycle times without stressing the robot structure • Less inertia to fight against • Motion where it actually matters, at the tool center point It is a reminder that many performance limits in robotics are not solved by bigger motors, but by smarter mechanics. ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

46,721 Aufrufe • vor 14 Tagen

Hardware Production 🤝 Robotics But these motors... are CRAZY synced: CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy. This is really important for making sure the machine does its job correctly. ✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate. ✅ Prevents the machine from getting damaged by keeping everything aligned. ✅ Allows the machine to work faster by making sure the motors are in sync. Synchronized CNC motors make sure machines work better, last longer, and create more precise products. —- Weekly robotics and AI insights. Subscribe free:

Hardware Production 🤝 Robotics But these motors... are CRAZY synced: CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy. This is really important for making sure the machine does its job correctly. ✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate. ✅ Prevents the machine from getting damaged by keeping everything aligned. ✅ Allows the machine to work faster by making sure the motors are in sync. Synchronized CNC motors make sure machines work better, last longer, and create more precise products. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

317,920 Aufrufe • vor 8 Monaten

🛠️ What if a robot could invent its own tools. And teach itself how to use them? That’s exactly what VLMgineer does: a new framework that lets Vision Language Models (VLMs) design physical tools and the actions to use them, entirely on their own. No templates. No human demonstrations. Just raw, AI-driven creativity. Why it matters ✅ Co-designs tools and actions together using VLMs, ensuring tight coupling between form and function ✅ Uses VLM-guided evolution (not random search) to refine designs intelligently ✅ Outperforms human-designed tools by +64.7% in task success across 12 RoboToolBench challenges ✅ Produces better-than-everyday tools for real manipulation tasks—measured in success rate and elegance It builds on the emerging trend of large-model-guided evolutionary design (like Eureka and AlphaEvolve) and brings it into physical robotics. It opens the door to general-purpose, automated hardware design, no strong priors needed. Code & paper: —- Weekly robotics and AI insights. Subscribe free:

🛠️ What if a robot could invent its own tools. And teach itself how to use them? That’s exactly what VLMgineer does: a new framework that lets Vision Language Models (VLMs) design physical tools and the actions to use them, entirely on their own. No templates. No human demonstrations. Just raw, AI-driven creativity. Why it matters ✅ Co-designs tools and actions together using VLMs, ensuring tight coupling between form and function ✅ Uses VLM-guided evolution (not random search) to refine designs intelligently ✅ Outperforms human-designed tools by +64.7% in task success across 12 RoboToolBench challenges ✅ Produces better-than-everyday tools for real manipulation tasks—measured in success rate and elegance It builds on the emerging trend of large-model-guided evolutionary design (like Eureka and AlphaEvolve) and brings it into physical robotics. It opens the door to general-purpose, automated hardware design, no strong priors needed. Code & paper: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

13,984 Aufrufe • vor 8 Monaten

A metal origami! 🪭 This method is called Hyperbolic Metal Forming and is hypnotizing to watch. Instead of shaping metal with slow mechanical force, HMF uses controlled shockwaves to form complex geometries at extreme speed, often without the need for heavy dies or post-processing. The result is stronger, lighter parts with shapes that are almost impossible using traditional stamping. That’s why you see it popping up in aerospace, automotive structures, and defense components. Think of it like metal origami, but driven by high-energy pulses instead of presses. A small reminder that some things in manufacturing come from physics, not just automation. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

A metal origami! 🪭 This method is called Hyperbolic Metal Forming and is hypnotizing to watch. Instead of shaping metal with slow mechanical force, HMF uses controlled shockwaves to form complex geometries at extreme speed, often without the need for heavy dies or post-processing. The result is stronger, lighter parts with shapes that are almost impossible using traditional stamping. That’s why you see it popping up in aerospace, automotive structures, and defense components. Think of it like metal origami, but driven by high-energy pulses instead of presses. A small reminder that some things in manufacturing come from physics, not just automation. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

567,473 Aufrufe • vor 8 Monaten

America and China build robots. Europe builds committees. Tesla just showed Optimus in pilot production. Humanoids being assembled like cars. Meanwhile, in Europe, we’re still arguing over regulation, ethics boards, and frameworks that nobody in the real world reads. This isn’t about copying the US. It’s about realizing that if we keep optimizing for safety over progress, we’ll end up safe... but irrelevant. I work in robotics companies every day. We have world-class talent here... But talent without permission to execute is just potential wasted. —- Weekly robotics and AI insights. Subscribe free:

America and China build robots. Europe builds committees. Tesla just showed Optimus in pilot production. Humanoids being assembled like cars. Meanwhile, in Europe, we’re still arguing over regulation, ethics boards, and frameworks that nobody in the real world reads. This isn’t about copying the US. It’s about realizing that if we keep optimizing for safety over progress, we’ll end up safe... but irrelevant. I work in robotics companies every day. We have world-class talent here... But talent without permission to execute is just potential wasted. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

184,082 Aufrufe • vor 9 Monaten

Why spend hundreds on videography, creators, and production when AI UGC can deliver authentic, high-converting content in minutes? ✅ No actors needed ✅ No filming setup ✅ Faster turnaround ✅ Budget-friendly ✅ Easy creative testing at scale All you need is a product URL, and SmartUGC AI handles the heavy lifting—helping generate ad-ready UGC-style videos that would normally take days to produce. For brands, marketers, and creators looking to move faster without sacrificing creativity, this is definitely worth trying. Start creating AI UGC here

Why spend hundreds on videography, creators, and production when AI UGC can deliver authentic, high-converting content in minutes? ✅ No actors needed ✅ No filming setup ✅ Faster turnaround ✅ Budget-friendly ✅ Easy creative testing at scale All you need is a product URL, and SmartUGC AI handles the heavy lifting—helping generate ad-ready UGC-style videos that would normally take days to produce. For brands, marketers, and creators looking to move faster without sacrificing creativity, this is definitely worth trying. Start creating AI UGC here

FATHELA ESQ

130,887 Aufrufe • vor 1 Monat

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 Aufrufe • vor 8 Monaten

Some uses of AI remind me why technology matters. After years of seeing automation focus on speed and cost, this is the kind of progress that stands out. China is now using AI-powered robots inside grain warehouses, places so dangerous that workers can sink into loose grain like quicksand or suffocate in dust explosions. These robots map, navigate, and level the grain autonomously, while humans stay safely behind monitors. It is a quiet revolution. Not about efficiency or profit, but about dignity and safety. This is the kind of automation I deeply believe in, where technology protects people before it replaces them. If AI should remove humans from danger first, which industry should it transform next? #AI #Automation #Robotics #ArtificialIntelligence #TechForGood #Innovation #FutureOfWork

Some uses of AI remind me why technology matters. After years of seeing automation focus on speed and cost, this is the kind of progress that stands out. China is now using AI-powered robots inside grain warehouses, places so dangerous that workers can sink into loose grain like quicksand or suffocate in dust explosions. These robots map, navigate, and level the grain autonomously, while humans stay safely behind monitors. It is a quiet revolution. Not about efficiency or profit, but about dignity and safety. This is the kind of automation I deeply believe in, where technology protects people before it replaces them. If AI should remove humans from danger first, which industry should it transform next? #AI #Automation #Robotics #ArtificialIntelligence #TechForGood #Innovation #FutureOfWork

Pascal Bornet

31,533 Aufrufe • vor 6 Monaten

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

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

CTO ROBOTICS Media

420,246 Aufrufe • vor 2 Monaten

Traditional factory automation follows three rules: 1. Minimise degrees of freedom 2. Minimise actuation 3. Minimise the requirement for feedback Let geometry and gravity do the hard work. This is a step feeder. Reciprocating plates push parts upward, one step at a time. Parts that sit stable on the ledge rise to the top. Parts that don't? They tip off and fall back into the hopper. A chain conveyor carries the parts along and a simple tool knocks off any that are misaligned. No robot. No vision. No gripper. Just continuous circulation until every part finds its perfect orientation. Bowl feeders, gravity slides, step feeders, escapements—all built on the same principle. Constrain movement. Let shape and weight do the sorting. Modern robotics throws sensors, actuators, and computing at every problem. Sometimes the smarter move is less.

Traditional factory automation follows three rules: 1. Minimise degrees of freedom 2. Minimise actuation 3. Minimise the requirement for feedback Let geometry and gravity do the hard work. This is a step feeder. Reciprocating plates push parts upward, one step at a time. Parts that sit stable on the ledge rise to the top. Parts that don't? They tip off and fall back into the hopper. A chain conveyor carries the parts along and a simple tool knocks off any that are misaligned. No robot. No vision. No gripper. Just continuous circulation until every part finds its perfect orientation. Bowl feeders, gravity slides, step feeders, escapements—all built on the same principle. Constrain movement. Let shape and weight do the sorting. Modern robotics throws sensors, actuators, and computing at every problem. Sometimes the smarter move is less.

Jack 🤖

467,448 Aufrufe • vor 8 Monaten

🚨 Claude just changed the game. All you need is: 💻 A laptop 🌐 Internet connection ⏰ 60 minutes a day That’s enough to build a $7,200/month online income stream using AI. No coding. No expensive setup. No years of experience. Most people still use AI for fun… But smart creators are quietly using Claude to: • Create digital products • Offer AI services • Write viral content • Automate work • Build online income streams Usually, I sell this detailed guide for $97… But today you can get it FREE. 🎁 Inside you'll discover: ✅ The exact asset ✅ My full workflow ✅ The Claude prompts I personally use ✅ How to scale to $10K/month ✅ How beginners can start fast Want it? ❤️ Like this post 💬 Comment “AI” ➕ Follow me to receive it in DM ⏳ Available FREE for 48 hours only.

🚨 Claude just changed the game. All you need is: 💻 A laptop 🌐 Internet connection ⏰ 60 minutes a day That’s enough to build a $7,200/month online income stream using AI. No coding. No expensive setup. No years of experience. Most people still use AI for fun… But smart creators are quietly using Claude to: • Create digital products • Offer AI services • Write viral content • Automate work • Build online income streams Usually, I sell this detailed guide for $97… But today you can get it FREE. 🎁 Inside you'll discover: ✅ The exact asset ✅ My full workflow ✅ The Claude prompts I personally use ✅ How to scale to $10K/month ✅ How beginners can start fast Want it? ❤️ Like this post 💬 Comment “AI” ➕ Follow me to receive it in DM ⏳ Available FREE for 48 hours only.

NadzAI

13,261 Aufrufe • vor 1 Monat

Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:

Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

38,234 Aufrufe • vor 6 Monaten

1/ World models are getting popular in robotics 🤖✨ But there’s a big problem: most are slow and break physical consistency over long horizons. 2/ Today we’re releasing Interactive World Simulator: An action-conditioned world model that supports stable long-horizon interaction. 3/ Key result: ✅ 10+ minutes of interactive prediction ✅ 15 FPS ✅ on a single RTX 4090🔥 4/ Why this matters: it unlocks two critical robotics applications: 🚀 Scalable data generation for policy training 🧪 Faithful policy evaluation 5/ You can play with our world model NOW at NO git clone, NO pip install, NO python. Just click and play! NOTE ⚠️ ALL videos here are generated purely by our model in pixel space! They are **NOT** from a real camera More details coming 👇 (1/9) #Robotics #AI #MachineLearning #WorldModels #RobotLearning #ImitationLearning

1/ World models are getting popular in robotics 🤖✨ But there’s a big problem: most are slow and break physical consistency over long horizons. 2/ Today we’re releasing Interactive World Simulator: An action-conditioned world model that supports stable long-horizon interaction. 3/ Key result: ✅ 10+ minutes of interactive prediction ✅ 15 FPS ✅ on a single RTX 4090🔥 4/ Why this matters: it unlocks two critical robotics applications: 🚀 Scalable data generation for policy training 🧪 Faithful policy evaluation 5/ You can play with our world model NOW at NO git clone, NO pip install, NO python. Just click and play! NOTE ⚠️ ALL videos here are generated purely by our model in pixel space! They are NOT from a real camera More details coming 👇 (1/9) #Robotics #AI #MachineLearning #WorldModels #RobotLearning #ImitationLearning

Yixuan Wang

129,102 Aufrufe • vor 5 Monaten

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

Miyu Horiuchi

90,718 Aufrufe • vor 6 Monaten

This drone doesn’t just carry objects it grabs them. 🤯 Meet Ring-Rotor, a retractable quadrotor developed by researchers at Zhejiang University. Instead of using a robotic arm, the drone changes its shape and surrounds the object, allowing it to grasp and transport different payloads in mid-air. The frame can also shrink by about 31%, helping it pass through tighter spaces. No hook. No cable. No external gripper. The drone itself becomes the gripper. 🎥 Media: Yuze Wu , Fei Gao & Zhejiang University ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #Robotics #DroneTechnology #AerialRobotics #AerialManipulation #Automation #Engineering #Innovation #FutureTech

This drone doesn’t just carry objects it grabs them. 🤯 Meet Ring-Rotor, a retractable quadrotor developed by researchers at Zhejiang University. Instead of using a robotic arm, the drone changes its shape and surrounds the object, allowing it to grasp and transport different payloads in mid-air. The frame can also shrink by about 31%, helping it pass through tighter spaces. No hook. No cable. No external gripper. The drone itself becomes the gripper. 🎥 Media: Yuze Wu , Fei Gao & Zhejiang University ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #Robotics #DroneTechnology #AerialRobotics #AerialManipulation #Automation #Engineering #Innovation #FutureTech

CTO ROBOTICS Media

11,680 Aufrufe • vor 11 Tagen

No robots. No vision. No AI. Just a vibratory bowl feeder, a 2-axis pick-and-place, a turntable, and a gravity slide. This is the automation that actually runs the world. Bowl feeders have been orienting parts since the 1950s. Vibration and geometry—nothing else. Parts walk up a spiral track and fall into line. Pneumatic cylinders move on two axes—up/down and rotate. Hard stops set the positions. No encoders. No feedback loops. Just air pressure hitting a mechanical limit. Air jets blow parts down the slides. Gravity gets a helping hand from a tiny pulse of air to keep the flow consistent. Inflexible? Absolutely. One product, one machine. But for high-volume production, nothing beats it on cost, speed, or reliability. The unsexy backbone of manufacturing.

No robots. No vision. No AI. Just a vibratory bowl feeder, a 2-axis pick-and-place, a turntable, and a gravity slide. This is the automation that actually runs the world. Bowl feeders have been orienting parts since the 1950s. Vibration and geometry—nothing else. Parts walk up a spiral track and fall into line. Pneumatic cylinders move on two axes—up/down and rotate. Hard stops set the positions. No encoders. No feedback loops. Just air pressure hitting a mechanical limit. Air jets blow parts down the slides. Gravity gets a helping hand from a tiny pulse of air to keep the flow consistent. Inflexible? Absolutely. One product, one machine. But for high-volume production, nothing beats it on cost, speed, or reliability. The unsexy backbone of manufacturing.

Jack 🤖

39,565 Aufrufe • vor 8 Monaten

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. Why this matters if you want robotics to deliver real value on your floor: • Handles mixed glass with random poses and no fixed fixtures. • Runs real grasp selection under noise, vibration and production variability. • Maintains throughput while avoiding breakage on a delicate material. • Shows mobile manipulation doing actual shift work instead of controlled lab runs. Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. The direction is clear. Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success. —- Weekly robotics and AI insights. Subscribe free:

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. Why this matters if you want robotics to deliver real value on your floor: • Handles mixed glass with random poses and no fixed fixtures. • Runs real grasp selection under noise, vibration and production variability. • Maintains throughput while avoiding breakage on a delicate material. • Shows mobile manipulation doing actual shift work instead of controlled lab runs. Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. The direction is clear. Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

24,743 Aufrufe • vor 8 Monaten