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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...

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Lukas Ziegler

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289,657 görüntüleme • 5 ay önce •via X (Twitter)

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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.
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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

453,518 görüntüleme • 1 yıl önce

Open-source dexterous hands with fingertip sensors! 🪬 ORCA Dexterity just released three dexterous hand models. Their mission: is to democratize robotic hand dexterity. They're sharing progress on exceptional, low-cost hardware and a software layer from low-level control to robotic hand learning. This is the open-source approach to dexterous manipulation. It has 83 taxels per finger with 0.1 N force detection which pretty is impressive for an open-source design. Tactile sensing is critical for dexterous manipulation, knowing contact forces enables gentle grasping, slip detection, and force-controlled assembly. Also 700g weight for the lite version makes it practical for mounting on robot arms without exceeding payload limits. Lower weight means faster movements and lower torque requirements. Open hardware accelerates robotics by letting researchers and builders modify designs for their specific needs without starting from scratch! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Open-source dexterous hands with fingertip sensors! 🪬 ORCA Dexterity just released three dexterous hand models. Their mission: is to democratize robotic hand dexterity. They're sharing progress on exceptional, low-cost hardware and a software layer from low-level control to robotic hand learning. This is the open-source approach to dexterous manipulation. It has 83 taxels per finger with 0.1 N force detection which pretty is impressive for an open-source design. Tactile sensing is critical for dexterous manipulation, knowing contact forces enables gentle grasping, slip detection, and force-controlled assembly. Also 700g weight for the lite version makes it practical for mounting on robot arms without exceeding payload limits. Lower weight means faster movements and lower torque requirements. Open hardware accelerates robotics by letting researchers and builders modify designs for their specific needs without starting from scratch! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

38,249 görüntüleme • 6 ay önce

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 interactions with the real world are key. Combining information from multiple senses helps to make better accuracy and robustness, but challenges like mismatched data and sensor errors complicate this process. Inspired by how animals navigate seamlessly, researchers developed predictive coding, a self-supervised learning method based on the brain's visual cortex model. It combines visual and tactile cues from a robot, outperforming existing methods in recognizing places, even when some sensors fail. What a great project from BristolRoboticsLab and Sheffield Robotics! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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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 interactions with the real world are key. Combining information from multiple senses helps to make better accuracy and robustness, but challenges like mismatched data and sensor errors complicate this process. Inspired by how animals navigate seamlessly, researchers developed predictive coding, a self-supervised learning method based on the brain's visual cortex model. It combines visual and tactile cues from a robot, outperforming existing methods in recognizing places, even when some sensors fail. What a great project from BristolRoboticsLab and Sheffield Robotics! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

36,775 görüntüleme • 8 ay önce

1/🧠Humans are the best robot data source — but video alone misses one thing: force. 2/🙁Tactile gloves capture force — but they're costly and block the real touch manipulation depends on. 3/💪Maybe the future of touch lives on your wrist: surface EMG reads the muscles that cause force — tactile sensing without ever touching a tactile sensor. 4/🔥Want a fully open-source framework — hardware + software — to train your own force-aware learn-from-human-data robot policy? 🚀We introduce ForceBand: Learning Forceful Manipulation with sEMG -- bring force into human videos with sEMG, for force-aware manipulation ⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Force Beyond Vision ✦ Free-Hand Force Sensing ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Low-Cost & Easy-to-Implement Let's squeeze every bit of signal out of human data, and let robots feel the force! 🌐 Website: 📄 Paper: 💻 Code: 🎥 Video: 🧵 1/n
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1/🧠Humans are the best robot data source — but video alone misses one thing: force. 2/🙁Tactile gloves capture force — but they're costly and block the real touch manipulation depends on. 3/💪Maybe the future of touch lives on your wrist: surface EMG reads the muscles that cause force — tactile sensing without ever touching a tactile sensor. 4/🔥Want a fully open-source framework — hardware + software — to train your own force-aware learn-from-human-data robot policy? 🚀We introduce ForceBand: Learning Forceful Manipulation with sEMG -- bring force into human videos with sEMG, for force-aware manipulation ⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Force Beyond Vision ✦ Free-Hand Force Sensing ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Low-Cost & Easy-to-Implement Let's squeeze every bit of signal out of human data, and let robots feel the force! 🌐 Website: 📄 Paper: 💻 Code: 🎥 Video: 🧵 1/n

Zhi (Leo) Wang

52,455 görüntüleme • 3 ay önce

The quadruped you can assemble yourself. Stella is an educational quadruped robot developed by Ahead. Designed for hands-on learning and self-assembly, the robot uses durable 3D-printed components. Ideal for all education levels, it is easy to repair, infinitely extendable, and equipped with advanced hardware and software. Despite its compact design, Stella is versatile, capable of operating in various environments and even carrying payloads like an optional robotic arm. A powerful tool for exploring robotics.
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The quadruped you can assemble yourself. Stella is an educational quadruped robot developed by Ahead. Designed for hands-on learning and self-assembly, the robot uses durable 3D-printed components. Ideal for all education levels, it is easy to repair, infinitely extendable, and equipped with advanced hardware and software. Despite its compact design, Stella is versatile, capable of operating in various environments and even carrying payloads like an optional robotic arm. A powerful tool for exploring robotics.

Circuit

23,215 görüntüleme • 1 yıl önce

The sense of touch is the most criminally under-explored modality in robotics. Imagine doing sleight of hand wearing thick oven mitts. That's exactly how a robot feels today if it were alive. A magnetic piece snapping into place, a paper cup peeling out of a stack, a USB negotiating its way into the port - all invisible to the camera. Learning how to feel must be a full-stack co-designed effort. We are open-sourcing a principled methodology called "T-Rex": 1. Tactile as first-class citizen of the model. Our mixture-of-transformer runs two clocks asynchronously: a slow visuomotor expert plans the motion, and a fast tactile expert refines it in real time with high-frequency corrections at 4 "touch ticks" per vision tick. Forces change faster than frames arrive, so the architecture had to as well. 2. Open data. The largest tactile dataset ever released to our knowledge: a 50-hour (~5,500 episodes) high-quality, carefully synchronized robot play corpus, collected on SOTA tactile hand hardware with 22 degrees of freedom. Available today on HuggingFace! 3. Training recipe: T-Rex extends our prior work, EgoScale. Human egocentric videos for pretraining, a diverse dose of tactile robot play for mid-training. Our experiments show this bridges contact-free pretraining to contact-rich manipulation remarkably well. Pixels are cheap and everywhere, but they run out of steam at the moment of contact. Tactile will carry the last mile. The next scaling curve will be measured in hours of touch. T-Rex is a great collaboration between NVIDIA and Berkeley: 🧵
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The sense of touch is the most criminally under-explored modality in robotics. Imagine doing sleight of hand wearing thick oven mitts. That's exactly how a robot feels today if it were alive. A magnetic piece snapping into place, a paper cup peeling out of a stack, a USB negotiating its way into the port - all invisible to the camera. Learning how to feel must be a full-stack co-designed effort. We are open-sourcing a principled methodology called "T-Rex": 1. Tactile as first-class citizen of the model. Our mixture-of-transformer runs two clocks asynchronously: a slow visuomotor expert plans the motion, and a fast tactile expert refines it in real time with high-frequency corrections at 4 "touch ticks" per vision tick. Forces change faster than frames arrive, so the architecture had to as well. 2. Open data. The largest tactile dataset ever released to our knowledge: a 50-hour (~5,500 episodes) high-quality, carefully synchronized robot play corpus, collected on SOTA tactile hand hardware with 22 degrees of freedom. Available today on HuggingFace! 3. Training recipe: T-Rex extends our prior work, EgoScale. Human egocentric videos for pretraining, a diverse dose of tactile robot play for mid-training. Our experiments show this bridges contact-free pretraining to contact-rich manipulation remarkably well. Pixels are cheap and everywhere, but they run out of steam at the moment of contact. Tactile will carry the last mile. The next scaling curve will be measured in hours of touch. T-Rex is a great collaboration between NVIDIA and Berkeley: 🧵

Jim Fan

175,338 görüntüleme • 1 ay önce

A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
3:58

A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

1,806,457 görüntüleme • 4 ay önce

Build a humanoid robot yourself! 🪚 OpenArm is an open-source humanoid robot. It comes with full CAD, control code, firmware, and simulation tools, everything needed to build, modify, and operate it. The arms are designed to be compliant and backdrivable. Teleoperation is supported, with force feedback and real-time gravity compensation so operators can guide the arm naturally. Super important, on the simulation side, OpenArm works with platforms like MuJoCo and Isaac Sim, letting developers test policies in virtual environments before running on hardware. Assemble it yourself from a kit or get it prebuilt, the goal is accessibility for research labs, small teams, and enthusiasts. The project is run by Enactic in Tokyo, Japan, and aims to lower the barrier for experimenting with dexterous manipulation. Let's put robotics in mainstream! 🔥🔥 🔗 Find the link here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:07

Build a humanoid robot yourself! 🪚 OpenArm is an open-source humanoid robot. It comes with full CAD, control code, firmware, and simulation tools, everything needed to build, modify, and operate it. The arms are designed to be compliant and backdrivable. Teleoperation is supported, with force feedback and real-time gravity compensation so operators can guide the arm naturally. Super important, on the simulation side, OpenArm works with platforms like MuJoCo and Isaac Sim, letting developers test policies in virtual environments before running on hardware. Assemble it yourself from a kit or get it prebuilt, the goal is accessibility for research labs, small teams, and enthusiasts. The project is run by Enactic in Tokyo, Japan, and aims to lower the barrier for experimenting with dexterous manipulation. Let's put robotics in mainstream! 🔥🔥 🔗 Find the link here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

68,628 görüntüleme • 13 gün önce

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 →
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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 görüntüleme • 4 ay önce

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

61,026 görüntüleme • 8 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

59,669 görüntüleme • 4 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 • 5 ay önce

No one could turn down a humanoid robot with human-like tactile sensitivity. JQ Industries equipped the Unitree G1 humanoid robot with a fabric-based electronic skin (piezoresistive flexible tactile sensors). A light touch can convert pressure, deformation, and slip into electrical signals, which are then processed by AI for real-time closed-loop control.(Integrates NVIDIA Isaac Sim) With this skin,humanoid robots start to behave more naturally: Touch sensing is no longer limited to dexterous hands,the entire body can feel, allowing the robot to stop or adjust posture instantly during collisions. It can also detect hugs, taps on the shoulder, or gentle touches, and respond in a softer, more human-like way. This matters especially in elder care and medical rehabilitation. A humanoid robot could gently help someone stand up, or control force precisely during assisted massage without harming the skin. Rigid mechanical systems deliver superhuman physical strength, while flexible sensing systems enable nuanced emotions. Electronic skin bridges high intelligence and high emotional intelligence.
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No one could turn down a humanoid robot with human-like tactile sensitivity. JQ Industries equipped the Unitree G1 humanoid robot with a fabric-based electronic skin (piezoresistive flexible tactile sensors). A light touch can convert pressure, deformation, and slip into electrical signals, which are then processed by AI for real-time closed-loop control.(Integrates NVIDIA Isaac Sim) With this skin,humanoid robots start to behave more naturally: Touch sensing is no longer limited to dexterous hands,the entire body can feel, allowing the robot to stop or adjust posture instantly during collisions. It can also detect hugs, taps on the shoulder, or gentle touches, and respond in a softer, more human-like way. This matters especially in elder care and medical rehabilitation. A humanoid robot could gently help someone stand up, or control force precisely during assisted massage without harming the skin. Rigid mechanical systems deliver superhuman physical strength, while flexible sensing systems enable nuanced emotions. Electronic skin bridges high intelligence and high emotional intelligence.

CyberRobo

22,118 görüntüleme • 5 ay önce

Force-sensing fingers! 🧤 Stanford researchers just released UMI-FT, a handheld data collection platform that puts compact six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose data. Many manipulation tasks require careful force modulation: too little force and the task fails, too much and you cause damage. But commercial force/torque sensors are expensive, bulky, and fragile, which has limited large-scale force-aware policy learning. UMI-FT changes the economics. The platform uses an iPhone for RGB vision, ultrawide RGB, depth, and pose via ARKit, with each finger sensorized using a CoinFT sensor to capture per-finger wrench information during manipulation. This multimodal data trains an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. The learned policy runs slowest and generates reference targets, while model-based compliance and force controllers provide delicate 6D compliance control and real-time force modulation. They tested on three contact-rich, force-sensitive tasks: whiteboard wiping (locate eraser, grasp, wipe until clean), skewering zucchini (grasp slice firmly, push onto stick until punctured), and lightbulb insertion (grasp bulb, align bayonet pin with socket slit, insert while overcoming spring force, rotate to light up). The results are clear. Policies without compliance struggle to modulate contact force and trigger safety faults from excessive force. Policies without force sensing fail to grasp unseen objects or resist reaction forces, causing slippage. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:29

Force-sensing fingers! 🧤 Stanford researchers just released UMI-FT, a handheld data collection platform that puts compact six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose data. Many manipulation tasks require careful force modulation: too little force and the task fails, too much and you cause damage. But commercial force/torque sensors are expensive, bulky, and fragile, which has limited large-scale force-aware policy learning. UMI-FT changes the economics. The platform uses an iPhone for RGB vision, ultrawide RGB, depth, and pose via ARKit, with each finger sensorized using a CoinFT sensor to capture per-finger wrench information during manipulation. This multimodal data trains an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. The learned policy runs slowest and generates reference targets, while model-based compliance and force controllers provide delicate 6D compliance control and real-time force modulation. They tested on three contact-rich, force-sensitive tasks: whiteboard wiping (locate eraser, grasp, wipe until clean), skewering zucchini (grasp slice firmly, push onto stick until punctured), and lightbulb insertion (grasp bulb, align bayonet pin with socket slit, insert while overcoming spring force, rotate to light up). The results are clear. Policies without compliance struggle to modulate contact force and trigger safety faults from excessive force. Policies without force sensing fail to grasp unseen objects or resist reaction forces, causing slippage. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

12,868 görüntüleme • 8 ay önce

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

Ilir Aliu

12,854 görüntüleme • 3 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 • 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 • 9 ay önce

NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."
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NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

Sawyer Merritt

624,910 görüntüleme • 8 ay önce

One tool to handle them all! 🧰 Traditional suction-based systems struggle with over 30% of SKUs. Mesh bags, loose apparel, and porous items often lead to failed picks or damaged goods. Nexera Robotics (Nexera Robotics) is pushing that ceiling to more than 95% by innovating the hardware, not just the software. Their solution? NeuraGrasp®. Instead of rigid fingers or air-dependent suction, NeuraGrasp uses a compliant membrane that conforms to any object. It creates a mechanical lock through friction and wrap-around contact, even on items that leak air or deform. Pressure is distributed by the material itself, meaning no complex force-feedback loops are needed to prevent crushing. It’s single tool can lift heavy payloads, yet pick a single peach without bruising it. A super important part is that no SKU-specific training and no tool changers required. The industry has spent years optimizing robot “brains” while ignoring the hands. Nexera is perfecting the touch to make robots more capable. Congrats Nexera Robotics team! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:59

One tool to handle them all! 🧰 Traditional suction-based systems struggle with over 30% of SKUs. Mesh bags, loose apparel, and porous items often lead to failed picks or damaged goods. Nexera Robotics (Nexera Robotics) is pushing that ceiling to more than 95% by innovating the hardware, not just the software. Their solution? NeuraGrasp®. Instead of rigid fingers or air-dependent suction, NeuraGrasp uses a compliant membrane that conforms to any object. It creates a mechanical lock through friction and wrap-around contact, even on items that leak air or deform. Pressure is distributed by the material itself, meaning no complex force-feedback loops are needed to prevent crushing. It’s single tool can lift heavy payloads, yet pick a single peach without bruising it. A super important part is that no SKU-specific training and no tool changers required. The industry has spent years optimizing robot “brains” while ignoring the hands. Nexera is perfecting the touch to make robots more capable. Congrats Nexera Robotics team! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,230 görüntüleme • 8 ay önce

This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction
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This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction

Techniahqrobot | humanoid robots

28,176 görüntüleme • 2 ay önce