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Deep in robotics. Bringing together founders, investors & operators.

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Tiny actuators. Accurate down to about 25 nanometers. Xeryon builds piezo actuators designed for positioning tasks where normal motors simply are not precise enough. • Repeatability around ±25 nm • Linear, rotary, and multi-DoF configurations • Used in metrology, semiconductor tooling, and laser systems At this scale, motion is no longer about speed or power. It is about staying exactly where you are supposed to be. —— Weekly robotics and AI insights. Subscribe free:

Tiny actuators. Accurate down to about 25 nanometers. Xeryon builds piezo actuators designed for positioning tasks where normal motors simply are not precise enough. • Repeatability around ±25 nm • Linear, rotary, and multi-DoF configurations • Used in metrology, semiconductor tooling, and laser systems At this scale, motion is no longer about speed or power. It is about staying exactly where you are supposed to be. —— Weekly robotics and AI insights. Subscribe free:

327,276 просмотров

You don’t need expensive hardware to build something that feels like real robotics. An Arduino, a servo, and an ultrasonic sensor can scan space and turn echoes into a simple radar view. It sweeps, measures distance, and visualizes it in real time. Cheap, simple, and a solid way to understand how machines sense the world. Limits show up fast, which is where better sensing and smarter software come in. Original build by SunFounder —— Weekly robotics and AI insights. Subscribe free:

You don’t need expensive hardware to build something that feels like real robotics. An Arduino, a servo, and an ultrasonic sensor can scan space and turn echoes into a simple radar view. It sweeps, measures distance, and visualizes it in real time. Cheap, simple, and a solid way to understand how machines sense the world. Limits show up fast, which is where better sensing and smarter software come in. Original build by SunFounder —— Weekly robotics and AI insights. Subscribe free:

875,292 просмотров

Robots are 3D printing full-size ship hulls. ㅤ CEAD’s Faber Navalis system uses robotic arms to print complete marine hulls up to 12 m long and 4 m wide in a single automated run—no molds, no segmentation, and no heavy manual assembly. ㅤ A 12-meter RIB-style prototype for the Royal Dutch Navy was printed in 8 days, tested during NATO’s BOMA exercise, and sailed for four days with the Navy in Den Helder. Traditional delivery timelines for similar vessels often run into years. ㅤ The hulls use glass-fiber-reinforced HDPro, roughly 10% lighter than comparable fiberglass while offering higher impact resistance than standard HDPE, plus UV stability and recyclability. Primary targets are USVs, RIBs, workboats, and patrol craft. —— Weekly robotics and AI insights? Subscribe free:

Robots are 3D printing full-size ship hulls. ㅤ CEAD’s Faber Navalis system uses robotic arms to print complete marine hulls up to 12 m long and 4 m wide in a single automated run—no molds, no segmentation, and no heavy manual assembly. ㅤ A 12-meter RIB-style prototype for the Royal Dutch Navy was printed in 8 days, tested during NATO’s BOMA exercise, and sailed for four days with the Navy in Den Helder. Traditional delivery timelines for similar vessels often run into years. ㅤ The hulls use glass-fiber-reinforced HDPro, roughly 10% lighter than comparable fiberglass while offering higher impact resistance than standard HDPE, plus UV stability and recyclability. Primary targets are USVs, RIBs, workboats, and patrol craft. —— Weekly robotics and AI insights? Subscribe free:

84,485 просмотров

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:

1,677,279 просмотров

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

819,110 просмотров

This was 25 years ago. I often think about that.

This was 25 years ago. I often think about that.

122,881 просмотров

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 breakthrough is about AI or advanced robotics... sometimes simple mechanical principles are the most efficient solution. —- Weekly robotics and AI insights. Subscribe free:

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 breakthrough is about AI or advanced robotics... sometimes simple mechanical principles are the most efficient solution. —- Weekly robotics and AI insights. Subscribe free:

532,401 просмотров

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:

317,920 просмотров

$1,000,000,000+ raised... How can a guy from a tiny town in Illinois, with no engineering degree, be the one building the most powerful humanoid company in the world? The story makes no sense. Until it does. [Long Thread 🧵]

$1,000,000,000+ raised... How can a guy from a tiny town in Illinois, with no engineering degree, be the one building the most powerful humanoid company in the world? The story makes no sense. Until it does. [Long Thread 🧵]

313,752 просмотров

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:

184,088 просмотров

The uncomfortable truth in robotics: Better tech does not win. Execution does. A small French team raised $20M and deployed in 100+ factories with ZERO hardware or manufacturing background. Here is how they did it 👇 [If you build robotics, save this thread. 🧵]

The uncomfortable truth in robotics: Better tech does not win. Execution does. A small French team raised $20M and deployed in 100+ factories with ZERO hardware or manufacturing background. Here is how they did it 👇 [If you build robotics, save this thread. 🧵]

153,085 просмотров

For 41 years, to find shortest paths in a graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:

For 41 years, to find shortest paths in a graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:

95,380 просмотров

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:

16,064 просмотров

It doesn’t look like much. But this small robot from Stanford might do for robotics what the Apple II did for computing... make it personal. 🧵 [📍 Save this... you’ll want to remember his name.]

It doesn’t look like much. But this small robot from Stanford might do for robotics what the Apple II did for computing... make it personal. 🧵 [📍 Save this... you’ll want to remember his name.]

91,480 просмотров

Mini 6 dof Arm. 3D printed planetary gearboxs & more… [📍GitHub link below ] A mini 6-axis arm driven by stepper motors with custom 3D printed split ring planetary gearboxs and an inverted belt differential wrist with custom bearings, driven by low-cost stepper motors and TMC5150 drivers. Custom firmware was written in C for the STM32 MCU on an BTT Octopus board, to allow for full closed loop PID control using AS5048a encoders daisy-chained over SPI. The controller takes joint targets and returns joint states to a Raspberry Pi 5 streamed over CAN bus. All credit to James Gullberg: 📍GitHub: —— Weekly robotics and AI insights. Subscribe free:

Mini 6 dof Arm. 3D printed planetary gearboxs & more… [📍GitHub link below ] A mini 6-axis arm driven by stepper motors with custom 3D printed split ring planetary gearboxs and an inverted belt differential wrist with custom bearings, driven by low-cost stepper motors and TMC5150 drivers. Custom firmware was written in C for the STM32 MCU on an BTT Octopus board, to allow for full closed loop PID control using AS5048a encoders daisy-chained over SPI. The controller takes joint targets and returns joint states to a Raspberry Pi 5 streamed over CAN bus. All credit to James Gullberg: 📍GitHub: —— Weekly robotics and AI insights. Subscribe free:

32,723 просмотров

Everyone: automation is boring. Me:

Everyone: automation is boring. Me:

88,100 просмотров

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

53,992 просмотров

A drone that flies, drives, and switches modes in 0.1 seconds: [Build it yourself: CAD + parts ⬇️] No extra actuators, no deformation, just clever mechanics and full control. DUAWLFIN is a ground-aerial robot with unified actuation: flying like a quadcopter, rolling like a car, and transitioning seamlessly between modes. ✅ Climbs 30° slopes ✅ Hits 2 m/s on wheels with just 15W ✅ Only 3% added energy in flight mode ✅ Mode switch in 0.1s ✅ Fully open-source and 3D-printable Perfect for urban logistics, indoor nav, or just rethinking what drones can be. Paper: Website: Build it yourself: CAD + parts list in the paper 📍 BOOKMARK FOR LATER This is how you merge air and ground without compromise. —- Weekly robotics and AI insights. Subscribe free:

A drone that flies, drives, and switches modes in 0.1 seconds: [Build it yourself: CAD + parts ⬇️] No extra actuators, no deformation, just clever mechanics and full control. DUAWLFIN is a ground-aerial robot with unified actuation: flying like a quadcopter, rolling like a car, and transitioning seamlessly between modes. ✅ Climbs 30° slopes ✅ Hits 2 m/s on wheels with just 15W ✅ Only 3% added energy in flight mode ✅ Mode switch in 0.1s ✅ Fully open-source and 3D-printable Perfect for urban logistics, indoor nav, or just rethinking what drones can be. Paper: Website: Build it yourself: CAD + parts list in the paper 📍 BOOKMARK FOR LATER This is how you merge air and ground without compromise. —- Weekly robotics and AI insights. Subscribe free:

69,193 просмотров

This. Is. Open. Source.

This. Is. Open. Source.

54,783 просмотров

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Do you call it 4D printing, if the platform moves as well 🤔 3D printing just got a serious upgrade A new five-axis printer can handle 70° overhangs, no support structures needed. This breaks traditional limitations and opens the door to more complex, curved designs. Perfect for engineers and designers pushing the boundaries of what’s possible. Exciting times for the future of additive manufacturing.

Do you call it 4D printing, if the platform moves as well 🤔 3D printing just got a serious upgrade A new five-axis printer can handle 70° overhangs, no support structures needed. This breaks traditional limitations and opens the door to more complex, curved designs. Perfect for engineers and designers pushing the boundaries of what’s possible. Exciting times for the future of additive manufacturing.

101,459 просмотров • 7 дней назад

Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, Yinghao Xu 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:

Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, Yinghao Xu 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:

113,821 просмотров • 10 дней назад

Everyone talks about humanoids. Meanwhile, Loki Robotics:

Everyone talks about humanoids. Meanwhile, Loki Robotics:

3,281,731 просмотров • 8 месяцев назад

Robotics companies: let us solve repetitive tasks like folding shirts. Meanwhile, the factory floor:

Robotics companies: let us solve repetitive tasks like folding shirts. Meanwhile, the factory floor:

1,657,828 просмотров • 4 месяцев назад

Just a couple of days ago, and now this??? 98.2% success in simulation?? But most impressively... 10 different tasks in a row! receiving that reward only once! after all 10 are done! First they showed that a smarter way of sampling experience lets robots learn precise assembly and hard locomotion purely in simulation, with zero real-world data and no demonstrations. Now the same team just released AlphaNIST, where one single policy trained from scratch with almost nothing solves all 20 NIST assembly tasks at once. Did I mention 98.2% success in simulation...? Sequences of ten different parts in a row, rewarded only at the very end. And the robot starts inventing its own techniques, different nut-turning motions in tight gaps, reorienting parts against the environment, deciding the order of work itself, none of which was programmed in. It feels like the AlphaGo moment, but for contact-rich assembly: simple objective, massive scale, and strategies that surprise even the people who trained it. Again, thanks to Octi Zhang (Octi Zhang) and the team! 📌 Website: Paper: Follow along if you are interested in robotics and AI! —— Weekly robotics and AI insights. Subscribe free:

Just a couple of days ago, and now this??? 98.2% success in simulation?? But most impressively... 10 different tasks in a row! receiving that reward only once! after all 10 are done! First they showed that a smarter way of sampling experience lets robots learn precise assembly and hard locomotion purely in simulation, with zero real-world data and no demonstrations. Now the same team just released AlphaNIST, where one single policy trained from scratch with almost nothing solves all 20 NIST assembly tasks at once. Did I mention 98.2% success in simulation...? Sequences of ten different parts in a row, rewarded only at the very end. And the robot starts inventing its own techniques, different nut-turning motions in tight gaps, reorienting parts against the environment, deciding the order of work itself, none of which was programmed in. It feels like the AlphaGo moment, but for contact-rich assembly: simple objective, massive scale, and strategies that surprise even the people who trained it. Again, thanks to Octi Zhang (Octi Zhang) and the team! 📌 Website: Paper: Follow along if you are interested in robotics and AI! —— Weekly robotics and AI insights. Subscribe free:

22,191 просмотров • 3 дней назад

A mini factory in a box – Runs 24/7 – Costs $11,950 / Double from $21,900 – Astra/Opus handles full deployment – Available now, 30-day money-back – Builds electronics, picks from bins, handles complex assembly… Credit: MicroFactory (Igor Kulakov (MicroFactory)) —— Weekly robotics and AI insights. Subscribe free:

A mini factory in a box – Runs 24/7 – Costs $11,950 / Double from $21,900 – Astra/Opus handles full deployment – Available now, 30-day money-back – Builds electronics, picks from bins, handles complex assembly… Credit: MicroFactory (Igor Kulakov (MicroFactory)) —— Weekly robotics and AI insights. Subscribe free:

39,947 просмотров • 6 дней назад

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:

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:

273,520 просмотров • 1 месяц назад

Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, Yinghao Xu 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:

Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, Yinghao Xu 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:

596,424 просмотров • 3 месяцев назад

You need $100 Billion to solve self-driving? Wrong. A small team of engineers in San Diego is doing it with a smartphone chip and a few cameras: ... for $999.🧵

You need $100 Billion to solve self-driving? Wrong. A small team of engineers in San Diego is doing it with a smartphone chip and a few cameras: ... for $999.🧵

1,525,606 просмотров • 10 месяцев назад

Every robot you see is a data firehose generating terabytes of chaos. This hidden crisis is the #1 reason robots fail, and it's costing the industry billions. You see hardware, but not the data swamp drowning engineers. In 2025, a quiet revolution is fixing it. Here’s how. 🧵

Every robot you see is a data firehose generating terabytes of chaos. This hidden crisis is the #1 reason robots fail, and it's costing the industry billions. You see hardware, but not the data swamp drowning engineers. In 2025, a quiet revolution is fixing it. Here’s how. 🧵

1,573,952 просмотров • 1 год назад

Impressing millions of people... robotics engineers are less impressed. Industrial robots have been capable of this for decades. The robot changes its orientation while keeping the tip of a long tool almost perfectly stationary above a fixed point. Peek engineering? Nope. (At least not today). Any decent 6-axis industrial robot should be able to do this. This is a standard application of Tool Center Point (TCP) calibration and coordinate transformations. Once the TCP is defined correctly, the controller can calculate the joint positions needed to rotate the tool around that point. Keeping a calibrated tool tip stationary in a demonstration is relatively straightforward. Maintaining that accuracy on a factory floor... with changing payloads, thermal drift, mechanical deflection and imperfect calibration, is a different engineering problem. There's also an important distinction between repeatability and absolute accuracy! A robot can return to the same position extremely consistently while still being several millimeters away from where its mathematical model predicts it should be. Which brings up a question worth discussing as robotics moves toward more autonomous systems: How much of industrial robotics is already a solved problem, and how much of the difficulty lies in getting robots to work reliably outside carefully controlled conditions? There are plenty of opportunities for startups these days, starting at the shop floor, speaking with the operators or those in charge of the numbers; identifying the biggest pain points is not easy, but definitely a good starting point to develop solutions. Follow for more topics about robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Impressing millions of people... robotics engineers are less impressed. Industrial robots have been capable of this for decades. The robot changes its orientation while keeping the tip of a long tool almost perfectly stationary above a fixed point. Peek engineering? Nope. (At least not today). Any decent 6-axis industrial robot should be able to do this. This is a standard application of Tool Center Point (TCP) calibration and coordinate transformations. Once the TCP is defined correctly, the controller can calculate the joint positions needed to rotate the tool around that point. Keeping a calibrated tool tip stationary in a demonstration is relatively straightforward. Maintaining that accuracy on a factory floor... with changing payloads, thermal drift, mechanical deflection and imperfect calibration, is a different engineering problem. There's also an important distinction between repeatability and absolute accuracy! A robot can return to the same position extremely consistently while still being several millimeters away from where its mathematical model predicts it should be. Which brings up a question worth discussing as robotics moves toward more autonomous systems: How much of industrial robotics is already a solved problem, and how much of the difficulty lies in getting robots to work reliably outside carefully controlled conditions? There are plenty of opportunities for startups these days, starting at the shop floor, speaking with the operators or those in charge of the numbers; identifying the biggest pain points is not easy, but definitely a good starting point to develop solutions. Follow for more topics about robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

15,228 просмотров • 4 дней назад

KoalaLifter... A robot to fix wind turbines Quite interesting, with an autonomous crane system ⁉️ Wind energy is a crucial renewable energy source This solution might help: > Erect turbine and replace spare parts without a crane > No height limits, no wind speed limits > More robust, reliable and safer You gotta like those innovations! Featured: KoalaLifter, GiGadgets,

KoalaLifter... A robot to fix wind turbines Quite interesting, with an autonomous crane system ⁉️ Wind energy is a crucial renewable energy source This solution might help: > Erect turbine and replace spare parts without a crane > No height limits, no wind speed limits > More robust, reliable and safer You gotta like those innovations! Featured: KoalaLifter, GiGadgets,

161,578 просмотров • 2 месяцев назад

People who’ve never set foot in a factory will never understand. I watched this three times.

People who’ve never set foot in a factory will never understand. I watched this three times.

429,637 просмотров • 11 месяцев назад

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

204,737 просмотров • 5 месяцев назад

A fully open source mocap system that works with cheap webcams: The FreeMoCap Project A free-and-open-source, hardware-and-software-agnostic, minimal-cost, research-grade, motion capture system and platform for decentralized scientific research, education, and training: —- Weekly robotics and AI insights. Subscribe free:

A fully open source mocap system that works with cheap webcams: The FreeMoCap Project A free-and-open-source, hardware-and-software-agnostic, minimal-cost, research-grade, motion capture system and platform for decentralized scientific research, education, and training: —- Weekly robotics and AI insights. Subscribe free:

29,683 просмотров • 22 дней назад

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

32,823 просмотров • 27 дней назад

For all my lifters: computer vision app to measure back curvature during deadlift! main technical highlights: — RF-DETR (Roboflow) to segment the person (great performance out-the-box with no additional training!) — YOLO11n (Ultralytics) for bounding box prediction around the barbell weight plate (trained on my own small dataset). — Mediapipe (Google) for pose landmark detection to guide the bounds for the line fitting. — Custom logic to fit a line across the back. about the plots on the right side: The Back Roundness Map shows the deviation of the estimated back curve from a line of best fit. Credit: Jeremy Park —— if it matters in AI or Robotics you'll see it here first:

For all my lifters: computer vision app to measure back curvature during deadlift! main technical highlights: — RF-DETR (Roboflow) to segment the person (great performance out-the-box with no additional training!) — YOLO11n (Ultralytics) for bounding box prediction around the barbell weight plate (trained on my own small dataset). — Mediapipe (Google) for pose landmark detection to guide the bounds for the line fitting. — Custom logic to fit a line across the back. about the plots on the right side: The Back Roundness Map shows the deviation of the estimated back curve from a line of best fit. Credit: Jeremy Park —— if it matters in AI or Robotics you'll see it here first:

234,710 просмотров • 8 месяцев назад

This is real time. You can see how stable the motion is. A system placing droplets between 1 nanoliter and 1 microliter inside a 96 well plate with almost perfect repeatability. No shake. No drift. No overshoot. The robot keeps the tip on track even at this scale where tiny vibrations normally ruin the result. Why this matters: •In wet lab automation, volume accuracy is everything •Nano scale tasks fail fast if the robot is even slightly off •Reliable deposition unlocks fast screening and repeatable experiments •You can run full workflows without manual checking or correction This is the kind of precision that turns lab robots from “helpful tool” into a full automation pipeline. Credit: M2-Automation GmbH —- Weekly robotics and AI insights. Subscribe free:

This is real time. You can see how stable the motion is. A system placing droplets between 1 nanoliter and 1 microliter inside a 96 well plate with almost perfect repeatability. No shake. No drift. No overshoot. The robot keeps the tip on track even at this scale where tiny vibrations normally ruin the result. Why this matters: •In wet lab automation, volume accuracy is everything •Nano scale tasks fail fast if the robot is even slightly off •Reliable deposition unlocks fast screening and repeatable experiments •You can run full workflows without manual checking or correction This is the kind of precision that turns lab robots from “helpful tool” into a full automation pipeline. Credit: M2-Automation GmbH —- Weekly robotics and AI insights. Subscribe free:

287,156 просмотров • 11 месяцев назад

Just look at these systems from CPS... they show what hyper-optimized automation actually looks like. Product arrives flat, a racetrack mechanism flips it upright, rows get grouped, and a bomb-door system drops the whole batch into the case. Fast, reliable, and dead simple. I don't see any serious humanoid adoption on the shop floor. Purpose-built machinery keep winning the speed and cost debate... at least for some more time to come. Packing flexible bags into cases looks easy until you try doing it at scale across different pouch formats. Seeing setups like this always brings up two questions for me. First, how does a flashy humanoid like Figure actually compare to a dedicated machine like this in the real world? Humanoids get all the media attention, but when it comes to raw throughput, uptime, and ROI on a specific packaging line, specialized kinematics usually wipe the floor with general-purpose robots. Second, how long until European regulations try to over-engineer safety or compliance rules around setups like this? While engineers are trying to innovate and speed up factory floors, we seem remarkably good at inventing new regulatory hurdles that slow down deployment across Europe. I’m curious how people who are working in those startups, packaging and industrial automation view this situation.

Just look at these systems from CPS... they show what hyper-optimized automation actually looks like. Product arrives flat, a racetrack mechanism flips it upright, rows get grouped, and a bomb-door system drops the whole batch into the case. Fast, reliable, and dead simple. I don't see any serious humanoid adoption on the shop floor. Purpose-built machinery keep winning the speed and cost debate... at least for some more time to come. Packing flexible bags into cases looks easy until you try doing it at scale across different pouch formats. Seeing setups like this always brings up two questions for me. First, how does a flashy humanoid like Figure actually compare to a dedicated machine like this in the real world? Humanoids get all the media attention, but when it comes to raw throughput, uptime, and ROI on a specific packaging line, specialized kinematics usually wipe the floor with general-purpose robots. Second, how long until European regulations try to over-engineer safety or compliance rules around setups like this? While engineers are trying to innovate and speed up factory floors, we seem remarkably good at inventing new regulatory hurdles that slow down deployment across Europe. I’m curious how people who are working in those startups, packaging and industrial automation view this situation.

56,157 просмотров • 1 месяц назад

Everyone talks about humanoids. Meanwhile, this KUKA…

Everyone talks about humanoids. Meanwhile, this KUKA…

200,268 просмотров • 9 месяцев назад