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This robot explores the world like a mouse! 🐁 That's an OG project, guess the year ;) A mouse-inspired bot called WhiskEye travels the world with 24 artificial whiskers and spiking neurons that mimic a nervous system. Recognizing familiar places is essential for engineering applications like robot navigation, where...

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Lukas Ziegler

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36,775 görüntüleme • 7 ay önce •via X (Twitter)

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This robot can juggle! 🤹🏼‍♀️ A great engineering showcase as a part of a master's thesis at the Brno University of Technology! CableEndy is a cable-driven parallel robot project at the B&R Industrial Automation Brno office. The robot was created most probably to make all professional Kendama players feel insecure :) Guess the year btw! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:53

This robot can juggle! 🤹🏼‍♀️ A great engineering showcase as a part of a master's thesis at the Brno University of Technology! CableEndy is a cable-driven parallel robot project at the B&R Industrial Automation Brno office. The robot was created most probably to make all professional Kendama players feel insecure :) Guess the year btw! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

31,455 görüntüleme • 2 ay önce

Unloading trucks with robots! 📦 Bastian Solutions has developed a mobile robot system that automates high-volume, floor-level unloading of trailers. Let's have a closer look at how it actually works! The robot seamlessly drives itself in and out of trailers and docks without any fixed infrastructure. This system picks up cases via an extendable conveyor, lifts them using a retractable mast, and accurately places them inside the trailer using a dedicated gripper. In the video you can see the reversed process, unloading of a truck. 🚛 It's guided by LiDAR-based navigation and an omnidirectional base, enabling fast, precise, and infrastructure-free operation. Integrates smoothly with existing systems like conveyors, scanners, sorters, and depalletizers. Every time I see a robot loading and unloading a truck, I'm like: we are getting there :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:23

Unloading trucks with robots! 📦 Bastian Solutions has developed a mobile robot system that automates high-volume, floor-level unloading of trailers. Let's have a closer look at how it actually works! The robot seamlessly drives itself in and out of trailers and docks without any fixed infrastructure. This system picks up cases via an extendable conveyor, lifts them using a retractable mast, and accurately places them inside the trailer using a dedicated gripper. In the video you can see the reversed process, unloading of a truck. 🚛 It's guided by LiDAR-based navigation and an omnidirectional base, enabling fast, precise, and infrastructure-free operation. Integrates smoothly with existing systems like conveyors, scanners, sorters, and depalletizers. Every time I see a robot loading and unloading a truck, I'm like: we are getting there :) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

65,144 görüntüleme • 1 ay önce

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 →
0:51

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

21,587 görüntüleme • 2 ay önce

Jeff Bezos tries the Tactile Telerobot. The Tactile Telerobot is a haptic telerobotic system capable of transmitting realistic touch feedback to an operator located anywhere in the world. Developed through a joint collaboration between Shadow Robot Company, HaptX, and SynTouch, the project’s initial research and development was funded by All Nippon Airways. -- Robotics teams are challenged to turn human-like dexterity from theory into real-world systems. While dexterous manipulation promises major impact across industries, a gap remains between capability and deployment. Shadow Robot helps close this gap through a dexterity-first, structured engagement model that transforms ideas into prototypes and scalable solutions. The "Trusted Hands: Engineering Collaboration with Shadow Robot" whitepaper highlights how collaboration and repeatable frameworks enable organizations to overcome barriers and advance dexterous robotics in new domains. Get it here: #robotics #robots #engineering #technology
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Jeff Bezos tries the Tactile Telerobot. The Tactile Telerobot is a haptic telerobotic system capable of transmitting realistic touch feedback to an operator located anywhere in the world. Developed through a joint collaboration between Shadow Robot Company, HaptX, and SynTouch, the project’s initial research and development was funded by All Nippon Airways. -- Robotics teams are challenged to turn human-like dexterity from theory into real-world systems. While dexterous manipulation promises major impact across industries, a gap remains between capability and deployment. Shadow Robot helps close this gap through a dexterity-first, structured engagement model that transforms ideas into prototypes and scalable solutions. The "Trusted Hands: Engineering Collaboration with Shadow Robot" whitepaper highlights how collaboration and repeatable frameworks enable organizations to overcome barriers and advance dexterous robotics in new domains. Get it here: #robotics #robots #engineering #technology

Wevolver

31,354 görüntüleme • 7 ay önce

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 →
0:29

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 →

Lukas Ziegler

10,554 görüntüleme • 9 ay önce

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
1:23

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

46,927 görüntüleme • 8 ay önce

Learn the differences between motion profiles! 💨 A real demonstration of engineering skills. In each robotic arm, in each legged robot, you will discover actuators that make the robot move! Let's compare two motion profiles: rectangular and trapezoid. - A rectangular motion profile is like a car with abrupt starts and stops, ideal for situations where speed is crucial. 🚙 - A trapezoid motion profile involves gradual acceleration and deceleration, offering a smoother ride, perfect for scenarios where precision and smoothness are essential, just like handling a pint of beer with care (I love your way of showing the difference! 🍻) P.S. Can you name different applications for these two motion profiles in robotics? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:32

Learn the differences between motion profiles! 💨 A real demonstration of engineering skills. In each robotic arm, in each legged robot, you will discover actuators that make the robot move! Let's compare two motion profiles: rectangular and trapezoid. - A rectangular motion profile is like a car with abrupt starts and stops, ideal for situations where speed is crucial. 🚙 - A trapezoid motion profile involves gradual acceleration and deceleration, offering a smoother ride, perfect for scenarios where precision and smoothness are essential, just like handling a pint of beer with care (I love your way of showing the difference! 🍻) P.S. Can you name different applications for these two motion profiles in robotics? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

112,320 görüntüleme • 8 ay önce

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

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

Lukas Ziegler

50,090 görüntüleme • 8 ay önce

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!
53:45

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

38,772 görüntüleme • 2 ay önce

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:
0:41

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

14,222 görüntüleme • 3 ay önce

About a robot that became a 3D printer! 🖨️ A team of designers in Shanghai developed a groundbreaking 6-axis robotic 3D printer inspired by spiderwebs during a three-week summer workshop at Tongji University. The project sought to merge design and fabrication processes, drawing from spider threads' biomimetic strength and structure. 🕸️ The printer utilizes a unique spindle-knot extrusion technique, enabling self-supporting structures and greater spatial flexibility than traditional layer-based 3D printing. The device features a robot from KUKA and a robotic end-effector with movable printheads, controlled by an Arduino-based system with precise temperature and extrusion controls. Despite being a work in progress, the project exemplifies the potential of combining robotics and 3D printing for advanced manufacturing applications.
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About a robot that became a 3D printer! 🖨️ A team of designers in Shanghai developed a groundbreaking 6-axis robotic 3D printer inspired by spiderwebs during a three-week summer workshop at Tongji University. The project sought to merge design and fabrication processes, drawing from spider threads' biomimetic strength and structure. 🕸️ The printer utilizes a unique spindle-knot extrusion technique, enabling self-supporting structures and greater spatial flexibility than traditional layer-based 3D printing. The device features a robot from KUKA and a robotic end-effector with movable printheads, controlled by an Arduino-based system with precise temperature and extrusion controls. Despite being a work in progress, the project exemplifies the potential of combining robotics and 3D printing for advanced manufacturing applications.

Lukas Ziegler

276,496 görüntüleme • 11 ay önce

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 görüntüleme • 4 ay önce

Open-source magnetic tactile sensor for $5! 🧲 Researchers introduced a magnetic tactile sensor that's low-cost, and easy to fabricate, democratizing tactile sensing for robotics. Operating in unstructured environments like homes and offices requires robots to sense forces during physical interaction. Yet the lack of a versatile, accessible tactile sensor has led to fragmented solutions and often force-unaware, sensorless approaches. Building an eFlesh sensor requires four components: a hobbyist 3D printer, off-the-shelf magnets (less than $5), a CAD model, and a magnetometer circuit board. The sensor is 3D printed with magnets embedded in the middle layer. Based on chosen mechanical properties, magnets displace in response to contact forces, measured by a magnetometer underneath. An open-source design tool converts simple OBJ/STL files into 3D-printable STLs. This enables application-specific sensors for robot hands, grippers, quadruped feet, and more. Slip detection generalizes to unseen objects with 95% accuracy. Visual-tactile control policies improve manipulation by 40% over vision-only baselines, achieving 90% success on precise tasks like plug insertion and credit card swiping. All design files, code, trained models, and conversion tools are openly available. Project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:57

Open-source magnetic tactile sensor for $5! 🧲 Researchers introduced a magnetic tactile sensor that's low-cost, and easy to fabricate, democratizing tactile sensing for robotics. Operating in unstructured environments like homes and offices requires robots to sense forces during physical interaction. Yet the lack of a versatile, accessible tactile sensor has led to fragmented solutions and often force-unaware, sensorless approaches. Building an eFlesh sensor requires four components: a hobbyist 3D printer, off-the-shelf magnets (less than $5), a CAD model, and a magnetometer circuit board. The sensor is 3D printed with magnets embedded in the middle layer. Based on chosen mechanical properties, magnets displace in response to contact forces, measured by a magnetometer underneath. An open-source design tool converts simple OBJ/STL files into 3D-printable STLs. This enables application-specific sensors for robot hands, grippers, quadruped feet, and more. Slip detection generalizes to unseen objects with 95% accuracy. Visual-tactile control policies improve manipulation by 40% over vision-only baselines, achieving 90% success on precise tasks like plug insertion and credit card swiping. All design files, code, trained models, and conversion tools are openly available. Project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

289,055 görüntüleme • 4 ay önce

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 →
0:24

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 görüntüleme • 5 ay önce

Awesome Robotics! 💾 Building your own robot just got a lot easier. MathWorks have released an open-source GitHub repository packed with robotics resources for anyone interested in getting hands-on. The repo includes examples for robot arms, ground vehicles, and drones, with projects that show how to connect with ROS and ROS2 or even deploy Simulink models directly as ROS nodes. There are also more advanced demos, like modeling off-road environments and testing navigation algorithms in photorealistic simulations. Everything is well-documented, with tutorials and links that make it easy to go from concept to prototype. 📑 Whether you’re a student, researcher, or just curious, there’s material here for every level. And since it’s an open community project, you can not only explore but also share your own contributions. For anyone looking to learn robotics by doing, this is a solid place to start! Here’s the link: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:08

Awesome Robotics! 💾 Building your own robot just got a lot easier. MathWorks have released an open-source GitHub repository packed with robotics resources for anyone interested in getting hands-on. The repo includes examples for robot arms, ground vehicles, and drones, with projects that show how to connect with ROS and ROS2 or even deploy Simulink models directly as ROS nodes. There are also more advanced demos, like modeling off-road environments and testing navigation algorithms in photorealistic simulations. Everything is well-documented, with tutorials and links that make it easy to go from concept to prototype. 📑 Whether you’re a student, researcher, or just curious, there’s material here for every level. And since it’s an open community project, you can not only explore but also share your own contributions. For anyone looking to learn robotics by doing, this is a solid place to start! Here’s the link: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,232 görüntüleme • 3 ay önce

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 →
0:59

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 görüntüleme • 6 ay önce

Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.
2:35

Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.

AI at Meta

453,343 görüntüleme • 1 yıl önce

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

Lukas Ziegler

59,835 görüntüleme • 8 ay önce

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.
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Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

384,925 görüntüleme • 1 yıl önce

Finally, someone automating the boring infrastructure stuff! 🫟 Road markings is tough. Nobody cares about them until they're gone. Then suddenly everyone's mad. But someone has to paint those lines, in traffic, in bad weather, repetitively. So 10LINES built a robot that just... does it. Workers don't have to stand in live traffic anymore. Digital mapping. Data-driven operations. The robot knows what it painted and where. Quite productive I would say :) This is the stuff nobody talks about in robotics. Not sexy like other embodiements, but a robot handling infrastructure work that's dangerous and tedious. Roads, parking lots, tunnels, all need this. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Finally, someone automating the boring infrastructure stuff! 🫟 Road markings is tough. Nobody cares about them until they're gone. Then suddenly everyone's mad. But someone has to paint those lines, in traffic, in bad weather, repetitively. So 10LINES built a robot that just... does it. Workers don't have to stand in live traffic anymore. Digital mapping. Data-driven operations. The robot knows what it painted and where. Quite productive I would say :) This is the stuff nobody talks about in robotics. Not sexy like other embodiements, but a robot handling infrastructure work that's dangerous and tedious. Roads, parking lots, tunnels, all need this. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

34,834 görüntüleme • 2 ay önce