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Smart mapping for robots exploring huge areas! 🗺️ This is called Rolling-Bonxai. When a drone or ground robot maps a massive area, say a whole city or massive warehouse, storing the entire map in memory gets expensive fast. You run out of RAM. This solution is simple, only keep...

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Lukas Ziegler

64,455 subscribers

15,699 просмотров • 3 месяцев назад •via X (Twitter)

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Фото профиля Inspector fomo
Inspector fomo3 месяцев назад

Rolling-Bonxai trades full-map RAM for disk I/O latency on reloads. That mirrors the sim-to-real transfer bet I wrote about last month. The real test is whether chunk swaps stay fast enough for continuous locomotion.

Фото профиля Chandra Shekhar 📟
Chandra Shekhar 📟3 месяцев назад

Where do you keep on finding banger upon banger codes and papers in robotics? Do you taste like the sauce because you are dipped in the sauce? Or is it because you know where to find the best sauce?

Фото профиля Damir Wallener
Damir Wallener3 месяцев назад

Chunked voxel grid. Watch for… - boundaries -> thrashing -> planning stalls - integration errors -> drift -> can’t find right chunks - map fragmentation -> loop closure -> ghosting - alt/range est. errors -> scale drift -> matching failures It’s simple until it meets the real world. 🙂

Фото профиля MendysRobotics
MendysRobotics3 месяцев назад

Sliding-window maps are the pragmatic answer for warehouse-scale AMRs too - you rarely need the full global map in RAM, just local occupancy plus anchors for loop closure. Curious how Rolling-Bonxai handles loop closure corrections once old chunks are already on disk.

Фото профиля Juan Jimeno
Juan Jimeno3 месяцев назад

@facontidavide check this out

Фото профиля Geospatial FM
Geospatial FM3 месяцев назад

That’s what they’ve always done with computer games

Фото профиля WanderingDuck
WanderingDuck3 месяцев назад

Your data is too dense. If the space is rectilinear just use a line and width and height.

Фото профиля Johannes Tscharn
Johannes Tscharn3 месяцев назад

Quite interesting how we developed solutions to these kinds of problems in video games.

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Seat assembling robots! 💺 This robot from KUKA performs an incredibly technology-heavy process. 🦺 It assembles the seats in the car. And it's so hard? Well, when the car moves along with the production line, things get more complicated. The robot also moves on a special additional axis, so it synchronizes with the production line. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Rope climbing robots! 🪢 One objective, multiple approaches to achieve it. Back in 2017 undergraduates from Mechanical Science and Engineering at the University of Illinois utilized many mechanical strategies and tools to create a full menagerie of rope-climbing robots. Amazing creativity and uniqueness of each solution. Which robot is your favorite? Personally, I would say the one at the end dressed as a monkey! 🦍 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Robotics doesn't need to be boring! 😴 Just imagine, you're an only child, you have no one to play football with. Then this fella comes in and you can train your dribbling skills as long as you want (or as long as the battery lasts). Anyway, pretty awesome robot for non-industrial use. Robots can be seen as companions! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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The robot is used to inspect the walls of ships. 🚢 The Magnet Crawler introduced by the University of Bremen is a lightweight and compact climbing robot for magnetic surfaces. The robot has two magnetic wheels driven by geared motors and an elastic tail to improve stability. In the front part of the robot, there is a miniature wireless camera. The robot is remotely teleoperated & the video signal from the camera is displayed to the operator on a portable screen. Let's wait and perhaps the robot will become more autonomous over time. Big fan of robots that make the world a safer place for humans. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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Robots can make building tall structures safer. 🧱 The fastening robot from Raise Robotics helps put up walls on high buildings without putting workers in danger. It uses robots to do the risky jobs people used to do. This means fewer accidents and injuries on construction sites. Workers can stay on the ground and control the robot from a safe place. My fear of height just got a fear of height! 🫣 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

34,832 просмотров • 22 дней назад

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Robotics is entering the mainstream! 👀 Let's accept this fact. Consumptionism – stonks. Possibilities (HW + SW) – stonks. Demographics - not stonks. Modern robots are often designed to interact with our legacy world, whether it's a household, a factory, or a shipyard. That means they often need to work with existing machines, tools, and spaces. In short: retrofitting. Improving old machines with new technologies without major interference in their operation. Modern way of integrating things. You don't have to change the entire environment to deploy a robot. The robot will adapt, just as human would when freshly hired. The promise is to have an army of robots to help with the world designed for us. I also believe that general purpose robots, are not a direct competition for single purpose machines (like a highly efficient specialized packaging machine, for example). It's like adding a robot to this carriage. Specialized machine could be a car ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

14,396 просмотров • 8 месяцев назад

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Fireside Alpha

205,967 просмотров • 2 месяцев назад

researchers gave a tiny local model human-style memory and its context limit basically stopped existing a team from MBZUAI, Princeton and Weizmann took a 1B model and rebuilt how it reads. instead of attending to everything at once, the model reads in 1,024 token chunks and passes the important stuff forward through an associative memory, the same way you carry the plot of a book between chapters without rereading them. the design mirrors human memory on purpose. full attention inside a chunk works as short-term memory. the module that carries information between chunks works as long-term memory. they even trained it like a person, starting with short easy texts and raising the difficulty gradually, because memory thrown into the deep end learns nothing. the numbers back it up. the normal model burns 40GB of GPU memory on a long document and collapses hard past its limit, dropping from 0.86 to 0.32 accuracy. the memory version holds 0.71 at double that length while using a flat 12GB no matter how long the input gets. it also needs about 30% fewer FLOPs. the part i keep thinking about is that nobody scaled anything here. they didn't build a bigger model, didn't stretch the window, didn't add compute. they looked at how a brain handles a long day and copied the architecture. a model small enough to run on a consumer gpu now survives documents its own architecture used to choke on. we keep treating intelligence as a compute problem. sometimes it's a memory problem.
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researchers gave a tiny local model human-style memory and its context limit basically stopped existing a team from MBZUAI, Princeton and Weizmann took a 1B model and rebuilt how it reads. instead of attending to everything at once, the model reads in 1,024 token chunks and passes the important stuff forward through an associative memory, the same way you carry the plot of a book between chapters without rereading them. the design mirrors human memory on purpose. full attention inside a chunk works as short-term memory. the module that carries information between chunks works as long-term memory. they even trained it like a person, starting with short easy texts and raising the difficulty gradually, because memory thrown into the deep end learns nothing. the numbers back it up. the normal model burns 40GB of GPU memory on a long document and collapses hard past its limit, dropping from 0.86 to 0.32 accuracy. the memory version holds 0.71 at double that length while using a flat 12GB no matter how long the input gets. it also needs about 30% fewer FLOPs. the part i keep thinking about is that nobody scaled anything here. they didn't build a bigger model, didn't stretch the window, didn't add compute. they looked at how a brain handles a long day and copied the architecture. a model small enough to run on a consumer gpu now survives documents its own architecture used to choke on. we keep treating intelligence as a compute problem. sometimes it's a memory problem.

Alex Veremeyenko

16,147 просмотров • 3 месяцев назад

A pentagon robot! 🖐🏽 Compared to typical SCARA models, it delivers higher speed, improved stability, better space efficiency, and can operate smoothly in tight areas, even through singularity positions. These robots are a good fit for handling complex tasks in challenging environments where conventional robots often fall short. Have you ever seen a robot like this? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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A pentagon robot! 🖐🏽 Compared to typical SCARA models, it delivers higher speed, improved stability, better space efficiency, and can operate smoothly in tight areas, even through singularity positions. These robots are a good fit for handling complex tasks in challenging environments where conventional robots often fall short. Have you ever seen a robot like this? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

42,565 просмотров • 7 месяцев назад

Cache is back in CS2, and this is exactly how you make a Counter-Strike map. They completely remade the map from the ground up, absolutely nothing remains from the version from the workshop. Gameplay on the new cache is very clean and smooth without too much physical or visual clutter. Many of the more complicated props have been replaced with simple boxes, such as the box on forklift in A site. There are a few clipping issues with props and walls, but I imagine these will get sorted out over time. The visual fidelity is amazing without sacrificing performance. This will be a very easy map to learn and play, as there isn't very much complexity at all. Many of the features we all kinda hated are gone, such as the window at Z connector mid, new box, and the railing at highway. If they remove a map for this, it should be a simple 4 square map, otherwise the map pool is going to get too filled with easy maps. Remember when we got like... NO updates. Now I'm getting exhausted covering all of this week after week. THANK GOODNESS.
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Cache is back in CS2, and this is exactly how you make a Counter-Strike map. They completely remade the map from the ground up, absolutely nothing remains from the version from the workshop. Gameplay on the new cache is very clean and smooth without too much physical or visual clutter. Many of the more complicated props have been replaced with simple boxes, such as the box on forklift in A site. There are a few clipping issues with props and walls, but I imagine these will get sorted out over time. The visual fidelity is amazing without sacrificing performance. This will be a very easy map to learn and play, as there isn't very much complexity at all. Many of the features we all kinda hated are gone, such as the window at Z connector mid, new box, and the railing at highway. If they remove a map for this, it should be a simple 4 square map, otherwise the map pool is going to get too filled with easy maps. Remember when we got like... NO updates. Now I'm getting exhausted covering all of this week after week. THANK GOODNESS.

WarOwl

366,296 просмотров • 5 месяцев назад

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

Lukas Ziegler

50,677 просмотров • 3 месяцев назад

SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.
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SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.

Sawyer Merritt

69,381 просмотров • 1 год назад

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A robot snake for the rescue! 🛟 Mechanum wheels, reconfigurable shape, camera set. These robots were developed by researchers at The University of Manchester. This machine is called MIRRAX-150, and it performs inspections as well as explorations. To map 3D point clouds, this machine uses low-cost components, 3D printing, and LiDAR technology. 🧭 Soon, this robot will be deployed on the Sellafield nuclear power station. Basically, it's a snake that changes its body! P.S. What use cases would fit these robots? 🐍 ~~~ ♻️ RT to help 1 robot find a new workplace.

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15,263 просмотров • 1 год назад

STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they get
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STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they get

NO1ennn

32,454 просмотров • 2 месяцев назад

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THE WOVEN ROBOT! Wow! The woven robot hand is here and it is absolutely game-changing. Allonic just braided thousands of tendon-like fibers around a 3D-printed skeleton in minutes. No joints. No screws. No human assembly. Pure biomimetic tissue that flexes, grips, and lasts like biology itself. This isn’t incremental. This is the breakthrough that collapses robot hand costs from $30,000 to pennies. The old world of expensive, fragile robotic limbs is over. Now humanoid robots can build more of themselves scalable, replicable, anywhere. Robots building Robots building Robots, yeah you get it. In your freaking garage! Massive opportunities are about to explode as work as we know it fades. Rent one, have it build your fleet, send it back, repeat. Thousands if you want. The age of woven machines begins.

Brian Roemmele

21,795 просмотров • 7 месяцев назад

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

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

Lukas Ziegler

20,457 просмотров • 5 месяцев назад

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

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

Lukas Ziegler

60,070 просмотров • 10 месяцев назад

Tom Campbell: "The planets and the universe only render themselves when people are looking at them. It's like the video game 'No Man's Sky'. We are living in a simulation." Our reality computes on the fly whatever a player sees. Land on a planet and you get animals, shrubs, lakes and land features. Look away and it is no longer computed. Nothing is saved in memory. Only a set of values that can be regenerated if someone looks again. "It only computed what the person saw. As soon as you looked away, it wasn't computed anymore." "There is no tree. There is no forest. If nobody is there and it is not being observed, it is not being rendered." Stars are just little dots of light. They do not have to come from a sun 20 light years away. When the sun comes up or you go to sleep they stop being rendered for you. We are living in a simulation computed by consciousness.
3:40

Tom Campbell: "The planets and the universe only render themselves when people are looking at them. It's like the video game 'No Man's Sky'. We are living in a simulation." Our reality computes on the fly whatever a player sees. Land on a planet and you get animals, shrubs, lakes and land features. Look away and it is no longer computed. Nothing is saved in memory. Only a set of values that can be regenerated if someone looks again. "It only computed what the person saw. As soon as you looked away, it wasn't computed anymore." "There is no tree. There is no forest. If nobody is there and it is not being observed, it is not being rendered." Stars are just little dots of light. They do not have to come from a sun 20 light years away. When the sun comes up or you go to sleep they stop being rendered for you. We are living in a simulation computed by consciousness.

Interstellar

12,507 просмотров • 1 месяц назад

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.
1:31

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,564 просмотров • 1 год назад