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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,...

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Lukas Ziegler

62,389 subscribers

17,570 Aufrufe • vor 1 Monat •via X (Twitter)

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7 Kommentare

Profilbild von Tashi Network
Tashi Networkvor 1 Monat

Pretending to fall asleep in my self-driving car so my Robot carries me to bed

Profilbild von Mr V
Mr Vvor 1 Monat

It’s really interesting how much this grasping concept has evolved.

Profilbild von Rob Schmidt
Rob Schmidtvor 1 Monat

Cool stuff. But definitely has an alien tentacle vibe going on.

Profilbild von Mirrorworld AI
Mirrorworld AIvor 1 Monat

Topology change still needs actuation. That adds complexity and failure points. 🔧🤖

Profilbild von Nico Schulz
Nico Schulzvor 1 Monat

Very cool

Profilbild von Isabelle E-Z
Isabelle E-Zvor 1 Monat

We keep celebrating robotic demos like this while automation in food processing outpaces any workforce transition plan. The tech advances, the jobs disappear.

Profilbild von Julien Eyraud
Julien Eyraudvor 1 Monat

@grok, c'est vrai ?

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MIT’s latest research just just gave Robots a great new skill to grip delicate yet heavy objects without breaking them. The big problem is robots often cannot pick up heavy but fragile things because gripping hard enough usually means crushing or slipping. A single gripper shape has to do 2 conflicting jobs, it must move freely to form a wrap, then resist big forces while staying gentle. Open-ended grippers can snake into place, but they usually need stiffness to hold, and stiffness puts load into a few high-pressure contact points. Very soft grippers spread pressure well, but they can buckle or slide when the object is heavy, so they fail at the holding stage. This new idea from MIT, called loop closure grasping, completely avoids that tradeoff. Loop closure grasping starts as an open loop, meaning the robot has a free tip that can snake around clutter and find a good wrap. Once the wrap is right, the tip locks back onto the base, turning the shape into a closed loop that surrounds the object. Now the load is carried mainly by tension, like a sling, so the loop can stay very floppy in bending and still hold strongly without pointy pressure. The prototype uses inflatable “vine” beams that grow from the tip, then a clamp and winch to fasten, tighten, and finally deflate for soft holding. That combo lets it do awkward grasps, like lifting a 6.8kg kettlebell from a cluttered bin or pulling an object from 3m away. --- Paper - science. org/doi/10.1126/sciadv.ady9581
1:08

MIT’s latest research just just gave Robots a great new skill to grip delicate yet heavy objects without breaking them. The big problem is robots often cannot pick up heavy but fragile things because gripping hard enough usually means crushing or slipping. A single gripper shape has to do 2 conflicting jobs, it must move freely to form a wrap, then resist big forces while staying gentle. Open-ended grippers can snake into place, but they usually need stiffness to hold, and stiffness puts load into a few high-pressure contact points. Very soft grippers spread pressure well, but they can buckle or slide when the object is heavy, so they fail at the holding stage. This new idea from MIT, called loop closure grasping, completely avoids that tradeoff. Loop closure grasping starts as an open loop, meaning the robot has a free tip that can snake around clutter and find a good wrap. Once the wrap is right, the tip locks back onto the base, turning the shape into a closed loop that surrounds the object. Now the load is carried mainly by tension, like a sling, so the loop can stay very floppy in bending and still hold strongly without pointy pressure. The prototype uses inflatable “vine” beams that grow from the tip, then a clamp and winch to fasten, tighten, and finally deflate for soft holding. That combo lets it do awkward grasps, like lifting a 6.8kg kettlebell from a cluttered bin or pulling an object from 3m away. --- Paper - science. org/doi/10.1126/sciadv.ady9581

Rohan Paul

1,632,855 Aufrufe • vor 9 Monaten

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today
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ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

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A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:
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A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:

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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 →
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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 Aufrufe • vor 2 Monaten

China’s pace of solar panel installation is extremely fast. 🇨🇳 So, at peak times, China has been installing on the order of ~100 panels per second. That’s a lot of repetitive work to be done. Robots are now handling the solar panels, making installation faster, safer, making installations faster, safer, and hands-free. Operators can focus on wiring and more technical demanding tasks. The placing process is delegated to robots, who can do it without getting exhausted. Due to safety reasons, I think that these applications still require a human-operator to be in the loop. But the biggest hurdle which is manually placing heavy panels is solved. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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China’s pace of solar panel installation is extremely fast. 🇨🇳 So, at peak times, China has been installing on the order of ~100 panels per second. That’s a lot of repetitive work to be done. Robots are now handling the solar panels, making installation faster, safer, making installations faster, safer, and hands-free. Operators can focus on wiring and more technical demanding tasks. The placing process is delegated to robots, who can do it without getting exhausted. Due to safety reasons, I think that these applications still require a human-operator to be in the loop. But the biggest hurdle which is manually placing heavy panels is solved. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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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 →
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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 →

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A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:
0:36

A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

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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. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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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. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

26,338 Aufrufe • vor 24 Tagen

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal
0:39

I’m thrilled to announce that we just released GraspGen, a multi-year project we have been cooking at NVIDIA Robotics 🚀 GraspGen: A Diffusion-Based Framework for 6-DOF Grasping Grasping is a foundational challenge in robotics 🤖 — whether for industrial picking or general-purpose humanoids. VLA + real data collection is all the rage now but is expensive and scales poorly for this task. For every new gripper and/or scene, you’ll have to recollect the dataset in this paradigm for the best perf. 💡Key Idea: Since grasping is such a well-defined task in simulation - why can’t we just scale synthetic data generation and train a generative model for grasping? By embracing modularity and standardized grasp formats, we can make this a turnkey technology that works zero-shot for multiple settings. GraspGen is a modular framework for diffusion-based 6-DOF grasp generation that scales across embodiment types, observability conditions, clutter, task complexity. Key Features: ✅ Multi-embodiment support: suction, parallel-jaw, and multi-fingered grippers ✅ Generalization to partial + complete 3D point clouds ✅ Generalization to single-objects + cluttered scenes ✅ Modular design uses other robotics modules and foundation models (SAM2, cuRobo, FoundationStereo, FoundationPose). This allows GraspGen to focus on only one thing - grasp generation ✅ Training recipe: grasp discriminator is trained with On-Generator data from the diffusion model - so that it learns to correct the mistakes (if any) of the diffusion generator ✅ Real-time performance (~20 Hz) before any GPU acceleration; low memory footprint 📊 Results: • SOTA on the FetchBench [Han et al. CoRL 2024] benchmark • Zero-shot sim-to-real transfer on unknown objects and cluttered scenes • Dataset of 53M simulated grasps across 8K objects from Objaverse 📄 arXiv: 🌐 Website: 💻 Code: A huge thank you to everyone involved in this journey — excited to see what the community builds on top of it! Joint work with Clemens Eppner , Balakumar Sundaralingam , Yu-Wei, Jun Yamada Wentao Yuan and other collaborators #robotics #diffusionmodels #physicalAI #simtoreal

Adithya Murali

24,347 Aufrufe • vor 1 Jahr

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 →
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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 Aufrufe • vor 8 Monaten

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 Aufrufe • vor 4 Monaten

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They’ve been repeating the same script for decades. Climate panic. Nuclear threats. Manufactured wars. The illusion of chaos… always followed by a “solution.” But this time, it’s different. We’ve reached the final loop, the one that leads to transhumanism. A new system is rising. And it has no place for the human soul as we know it. What they call “progress” is just the slow erasure of everything human. This is not a conspiracy. It’s a pattern. And it’s almost complete. Watch my series

Dimitri Revolt

62,666 Aufrufe • vor 1 Jahr

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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 Aufrufe • vor 9 Monaten

Robots are coming for one of agriculture's most delicate jobs. Picking berries! 🍓 This is Fieldwork Robotics' Fieldworker 1 platform, designed to autonomously harvest raspberries at speeds comparable to human workers. That might sound simple, but berry picking is one of the hardest challenges in agricultural robotics. The robot needs to identify ripe fruit, avoid damaging fragile berries, and operate efficiently in a constantly changing environment. Fieldworker 1 uses custom AI systems and spectral analysis to assess ripeness and is designed so that a single operator can supervise multiple robots in the field. Agriculture continues to face labor shortages across many regions, while growers are under pressure to improve productivity and reduce waste. That's creating a strong demand for automation solutions that can work alongside existing operations. Fieldwork is already running trials with Costa Group, one of Australia's largest fruit growers, and joins a growing group of companies proving that robotic harvesting is moving from research projects into commercial deployments. That's a great market to look out for robotic solutions. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:24

Robots are coming for one of agriculture's most delicate jobs. Picking berries! 🍓 This is Fieldwork Robotics' Fieldworker 1 platform, designed to autonomously harvest raspberries at speeds comparable to human workers. That might sound simple, but berry picking is one of the hardest challenges in agricultural robotics. The robot needs to identify ripe fruit, avoid damaging fragile berries, and operate efficiently in a constantly changing environment. Fieldworker 1 uses custom AI systems and spectral analysis to assess ripeness and is designed so that a single operator can supervise multiple robots in the field. Agriculture continues to face labor shortages across many regions, while growers are under pressure to improve productivity and reduce waste. That's creating a strong demand for automation solutions that can work alongside existing operations. Fieldwork is already running trials with Costa Group, one of Australia's largest fruit growers, and joins a growing group of companies proving that robotic harvesting is moving from research projects into commercial deployments. That's a great market to look out for robotic solutions. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

25,493 Aufrufe • vor 2 Monaten

A robot that will bend metal for you! 🤘🏼 This is what optimized robotic press brake operation looks like. A well-tuned program, an additional seventh axis for extended reach, smart gripper design, and a workpiece reversal mechanism to handle parts without human intervention. This combination is invaluable for automated press brake operation. Cycle time is on fire! 🔥 KUKA robots are a solid choice for metal forming. They're built for high-payload, high-precision tasks and have a strong track record in automotive and heavy industry applications where reliability matters the most! :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:22

A robot that will bend metal for you! 🤘🏼 This is what optimized robotic press brake operation looks like. A well-tuned program, an additional seventh axis for extended reach, smart gripper design, and a workpiece reversal mechanism to handle parts without human intervention. This combination is invaluable for automated press brake operation. Cycle time is on fire! 🔥 KUKA robots are a solid choice for metal forming. They're built for high-payload, high-precision tasks and have a strong track record in automotive and heavy industry applications where reliability matters the most! :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

38,749 Aufrufe • vor 8 Monaten

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.
0:27

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,698 Aufrufe • vor 6 Monaten

That's how parts get inspected! 🪡 The pace is ridiculous. Imagine people still doing this instead of machines. Renishaw’s system is one of the biggest shifts in coordinate-measuring machines (CMMs). Instead of moving the entire machine to capture each point, REVO uses a 5-axis scanning head that collects data through rapid angular motion. Combined with automated probe and stylus changes, the system can handle complex geometries and full surface scans that traditionally required slow, point-by-point probing. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:49

That's how parts get inspected! 🪡 The pace is ridiculous. Imagine people still doing this instead of machines. Renishaw’s system is one of the biggest shifts in coordinate-measuring machines (CMMs). Instead of moving the entire machine to capture each point, REVO uses a 5-axis scanning head that collects data through rapid angular motion. Combined with automated probe and stylus changes, the system can handle complex geometries and full surface scans that traditionally required slow, point-by-point probing. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

243,475 Aufrufe • vor 10 Monaten

A robot that drives straight up a wall! 🧱 VertiGo is a robot built by Disney Research with ETH Zürich. Most wall-climbing robots hold on. Suction cups, magnets, dry adhesives. All of them need a surface that cooperates, and none of them solve the genuinely hard part, which is getting from the floor onto the wall in the first place. Two tiltable propellers generate thrust into the wall, pressing four wheels against it hard enough to drive. Each propeller has two degrees of freedom for aiming that thrust, and one pair of wheels steers. The rear propeller pushes the robot toward the wall while the front propeller pushes upward, and the combination flips it onto the vertical surface. One propeller can hold you on a wall. It takes two to get you there. This is from the end of 2015, which is worth saying out loud. A decade later it's still an awesome project in robotics! :) ~~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:33

A robot that drives straight up a wall! 🧱 VertiGo is a robot built by Disney Research with ETH Zürich. Most wall-climbing robots hold on. Suction cups, magnets, dry adhesives. All of them need a surface that cooperates, and none of them solve the genuinely hard part, which is getting from the floor onto the wall in the first place. Two tiltable propellers generate thrust into the wall, pressing four wheels against it hard enough to drive. Each propeller has two degrees of freedom for aiming that thrust, and one pair of wheels steers. The rear propeller pushes the robot toward the wall while the front propeller pushes upward, and the combination flips it onto the vertical surface. One propeller can hold you on a wall. It takes two to get you there. This is from the end of 2015, which is worth saying out loud. A decade later it's still an awesome project in robotics! :) ~~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

120,891 Aufrufe • vor 10 Tagen