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Tactile Diffusion generates synthetic tactile images from sim data, capturing the complex illumination of the gel deformation. This research from UW & Meta AI is the first method using diffusion to close the sim2real gap for vision-based tactile sensing. Read the paper ⬇️

AI at Meta

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100,164 次观看 • 3 年前 •via X (Twitter)

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FreddieK8883 年前

#iExec's decentralized infrastructure can provide Meta with a secure platform for deploying their #NFTs and #metaverse applications. This can help to ensure that the applications are resistant to attacks and can provide a better user experience. $RLC

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a(b)3 年前

@freecs_org

🌹KINGof KINGS🌹LORD of LORDS🌹IAM🌹🦁🌹WHO IAM🌹 的头像

🌹KINGof KINGS🌹LORD of LORDS🌹IAM🌹🦁🌹WHO IAM🌹3 年前

@ylecun IAMazing🌹 🌹🕊️🦁🔥🔥🔥

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Lucid Scientific, Inc.1 年前

Expand the possibilities of your metabolic research. Resipher tracks real-time cellular oxygen consumption in standard 96-well plates, delivering continuous real-time data directly from your incubator. Request a free virtual demo or quote today >>

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Quick AI News3 年前

Wow! This is incredible! This research is a major step forward in closing the sim2real gap for vision-based tactile sensing. Exciting times ahead for AI!

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AI at Meta1 年前

Introducing Meta Perception Language Model (PLM): an open & reproducible vision-language model tackling challenging visual tasks. Learn more about how PLM can help the open source community build more capable computer vision systems. Read the research paper, and download the code and dataset:

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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.

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 次观看 • 1 年前

Introducing LocoTouch: Quadrupedal robots equipped with tactile sensing can now transport unsecured objects — no mounts, no straps. The tactile policy transfers zero-shot from sim to real. Core Task-Agnostic Features: 1. High-fidelity contact simulation for distributed tactile sensors 2. Adaptive gait reward for symmetric, frequency-adaptive locomotion Website: Paper:

Introducing LocoTouch: Quadrupedal robots equipped with tactile sensing can now transport unsecured objects — no mounts, no straps. The tactile policy transfers zero-shot from sim to real. Core Task-Agnostic Features: 1. High-fidelity contact simulation for distributed tactile sensors 2. Adaptive gait reward for symmetric, frequency-adaptive locomotion Website: Paper:

Changyi Lin

38,494 次观看 • 1 年前

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

Lerrel Pinto

84,611 次观看 • 3 年前

More beautiful robot hands. This one from Sharpa The hand uses what they call a Dynamic Tactile Array, meaning vision and touch work together and their SaTA (Spatially-anchored Tactile Awareness) algorithm. Each fingertip includes a mini camera and 1,000+ tactile pixels, hitting 0.005N pressure sensitivity. With 22 DOF and 6D force sensing, it can do everything from eggs to tools.

More beautiful robot hands. This one from Sharpa The hand uses what they call a Dynamic Tactile Array, meaning vision and touch work together and their SaTA (Spatially-anchored Tactile Awareness) algorithm. Each fingertip includes a mini camera and 1,000+ tactile pixels, hitting 0.005N pressure sensitivity. With 22 DOF and 6D force sensing, it can do everything from eggs to tools.

Rohan Paul

41,775 次观看 • 8 个月前

I'm thrilled to announce the launch of ⚡️Flash Diffusion from Jasper! Earlier this year, with our acquisition of Clipdrop, we launched the Jasper AI Research Lab in Paris. Today, we are excited to release our first piece of groundbreaking research: the open-source distillation method, "Flash Diffusion". Flash Diffusion accelerates inference by 500%, reduces computing costs, and produces higher-quality image outputs. Dive into the details and discover how Flash Diffusion is set to revolutionize the field of AI and image synthesis. Read all about it here: Try a demo on Hugging Face:

I'm thrilled to announce the launch of ⚡️Flash Diffusion from Jasper! Earlier this year, with our acquisition of Clipdrop, we launched the Jasper AI Research Lab in Paris. Today, we are excited to release our first piece of groundbreaking research: the open-source distillation method, "Flash Diffusion". Flash Diffusion accelerates inference by 500%, reduces computing costs, and produces higher-quality image outputs. Dive into the details and discover how Flash Diffusion is set to revolutionize the field of AI and image synthesis. Read all about it here: Try a demo on Hugging Face:

Timothy Young

10,093 次观看 • 2 年前

Most of today's AI can see the world, but it doesn’t **feel** it. Capturing the sense of touch is crucial for dexterous robotic manipulation, user modeling, and understanding physical interactions. Introducing OpenTouch: bringing full-hand tactile sensing into real-world AI🖐️ OpenTouch is collected in-the-wild using tactile sensing gloves, hand pose tracking gloves, and egocentric glasses. It includes: • 5 hours of real-world data, • 3 hours densely annotated contact-rich interactions, • 2,900 curated interaction clips, • across 800 objects, 14 environments, and 29 grasp types. all open at:

Most of today's AI can see the world, but it doesn’t feel it. Capturing the sense of touch is crucial for dexterous robotic manipulation, user modeling, and understanding physical interactions. Introducing OpenTouch: bringing full-hand tactile sensing into real-world AI🖐️ OpenTouch is collected in-the-wild using tactile sensing gloves, hand pose tracking gloves, and egocentric glasses. It includes: • 5 hours of real-world data, • 3 hours densely annotated contact-rich interactions, • 2,900 curated interaction clips, • across 800 objects, 14 environments, and 29 grasp types. all open at:

Paul Liang

48,581 次观看 • 5 个月前

My team at FAIR AI at Meta is hiring postdocs. We are working in the space of dexterous manipulation and tactile sensing. If you are interested reach out / apply:

My team at FAIR AI at Meta is hiring postdocs. We are working in the space of dexterous manipulation and tactile sensing. If you are interested reach out / apply:

Mustafa Mukadam

40,587 次观看 • 2 年前

Watch as Sharpa North assembles a computer autonomously! From precise positioning to screwing and wire clipping, North executes complex, contact-rich tasks powered by tactile feedback. Dexterous hands + tactile AI are what make real-world task execution possible. This is the shift from simple automation to real-world robotics. #SharpaRobotics

Watch as Sharpa North assembles a computer autonomously! From precise positioning to screwing and wire clipping, North executes complex, contact-rich tasks powered by tactile feedback. Dexterous hands + tactile AI are what make real-world task execution possible. This is the shift from simple automation to real-world robotics. #SharpaRobotics

Sharpa

28,164 次观看 • 6 个月前

When you snap overlays to walls, don’t forget you benefit from tactile feedback if the computer vision is good enough 🥹

When you snap overlays to walls, don’t forget you benefit from tactile feedback if the computer vision is good enough 🥹

Brad Lynch

72,267 次观看 • 1 年前

Touch alone isn’t enough. 🖐️ For robotics, tactile intelligence truly levels up when touch gains spatial meaning. At #ICRA2026, we dove into the core concept behind SaTA: Spatially-anchored Tactile Awareness for robust, dexterous manipulation. Read it here: The challenge is fundamental: a robot shouldn’t just register a touch, it needs to understand exactly where that contact occurs relative to its fingers, joints, and overall hand structure. This is the missing link that turns raw data into precise, real-time adjustments during manipulation. Why does this matter? Because the most complex part of any manipulation task happens when vision is at its least reliable, that final millimeter before insertion, sliding, gripping, or fine-tuning. At Sharpa, this is exactly why we’re building tactile hands and tactile AI in tandem. 🚀 #Sharpa #Robotics #EmbodiedAI #TactileIntelligence #ICRA2026 #DexterousManipulation 📷

Touch alone isn’t enough. 🖐️ For robotics, tactile intelligence truly levels up when touch gains spatial meaning. At #ICRA2026, we dove into the core concept behind SaTA: Spatially-anchored Tactile Awareness for robust, dexterous manipulation. Read it here: The challenge is fundamental: a robot shouldn’t just register a touch, it needs to understand exactly where that contact occurs relative to its fingers, joints, and overall hand structure. This is the missing link that turns raw data into precise, real-time adjustments during manipulation. Why does this matter? Because the most complex part of any manipulation task happens when vision is at its least reliable, that final millimeter before insertion, sliding, gripping, or fine-tuning. At Sharpa, this is exactly why we’re building tactile hands and tactile AI in tandem. 🚀 #Sharpa #Robotics #EmbodiedAI #TactileIntelligence #ICRA2026 #DexterousManipulation 📷

Sharpa

15,168 次观看 • 3 个月前

Seeing touch without pressing down! 🫆 Most tactile sensors rely on deformation. Press down, deform the sensor, measure the deformation. But light contact, touching water, cream, soft films, doesn't deform anything. The sensor sees nothing. LightTact flips the approach. It uses optical detection instead. An ambient-blocking configuration suppresses external light and internal illumination everywhere except at actual contact points. Only scattered light from true contact gets through. The results? High-contrast raw images. Non-contact pixels stay near-black. Contact pixels show the natural appearance of whatever's touching. This works across material properties, contact forces, surface colors, and lighting conditions. Water, facial cream, thin films, liquids, all become visible the moment they touch. The sensor outputs spatially aligned visual-tactile images. Vision-language models can read them directly. Here's the paper: Changyi Lin and team - hats off ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Seeing touch without pressing down! 🫆 Most tactile sensors rely on deformation. Press down, deform the sensor, measure the deformation. But light contact, touching water, cream, soft films, doesn't deform anything. The sensor sees nothing. LightTact flips the approach. It uses optical detection instead. An ambient-blocking configuration suppresses external light and internal illumination everywhere except at actual contact points. Only scattered light from true contact gets through. The results? High-contrast raw images. Non-contact pixels stay near-black. Contact pixels show the natural appearance of whatever's touching. This works across material properties, contact forces, surface colors, and lighting conditions. Water, facial cream, thin films, liquids, all become visible the moment they touch. The sensor outputs spatially aligned visual-tactile images. Vision-language models can read them directly. Here's the paper: Changyi Lin and team - hats off ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

207,776 次观看 • 2 个月前

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

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

52,379 次观看 • 2 个月前

Tactile sim-to-real has been the cost floor for a decade. Cheap to simulate a world, impossible to simulate a fingertip. So the field routed around it with grippers and vision only: good demos, no generalization. Sharpa’s TacMap changes the math: map object-to-sensor penetration depth to a 2D deformation image. Contact error 85%. Tactile data stops being a collection problem and becomes a compute problem. And compute scales. The teams that win dexterity won't log the most teleoperated hours. They'll generate contact data faster than rivals can collect it. h/t:

Tactile sim-to-real has been the cost floor for a decade. Cheap to simulate a world, impossible to simulate a fingertip. So the field routed around it with grippers and vision only: good demos, no generalization. Sharpa’s TacMap changes the math: map object-to-sensor penetration depth to a 2D deformation image. Contact error 85%. Tactile data stops being a collection problem and becomes a compute problem. And compute scales. The teams that win dexterity won't log the most teleoperated hours. They'll generate contact data faster than rivals can collect it. h/t:

Cybernetic Labs

15,305 次观看 • 1 个月前

You do not have to read a research paper from start to end for the first pass. This is a huge academic misconception. There are key parts of a paper and a specific order that students should be taught. ▶️ Here's how to read a research paper quickly using AI:

You do not have to read a research paper from start to end for the first pass. This is a huge academic misconception. There are key parts of a paper and a specific order that students should be taught. ▶️ Here's how to read a research paper quickly using AI:

Amina Yonis, PhD

40,764 次观看 • 2 年前

Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:

Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:

Stability AI

1,024,725 次观看 • 2 年前

Here’s the windmill assembly demo we showed at CES 2026 — the one no one saw coming. North executes a fully autonomous, long-horizon dexterous sequence with sustained hand–eye–tactile coordination and assembly-level precision enabled by tactile feedback. It’s also robust to disturbance: you can reposition the objects, and North will still identify them and recover the task. This is powered by CraftNet (VTLA) — using tactile feedback to continuously fine-tune the last-millimeter interaction, enabling reliable execution across 30+ steps. Read more about CraftNet: #Sharpa #SharpaWave #SharpaNorth #CraftNet #System0 #CES2026

Here’s the windmill assembly demo we showed at CES 2026 — the one no one saw coming. North executes a fully autonomous, long-horizon dexterous sequence with sustained hand–eye–tactile coordination and assembly-level precision enabled by tactile feedback. It’s also robust to disturbance: you can reposition the objects, and North will still identify them and recover the task. This is powered by CraftNet (VTLA) — using tactile feedback to continuously fine-tune the last-millimeter interaction, enabling reliable execution across 30+ steps. Read more about CraftNet: #Sharpa #SharpaWave #SharpaNorth #CraftNet #System0 #CES2026

Sharpa

88,136 次观看 • 8 个月前

Robot hands still break most simulators. Meta Reality Labs just open-sourced Project SuperDex — a contact-first physics engine built for the hard part: multi-finger grasps, in-hand reorientation, soft contact, tactile sensing. VR teleop for cheap demo data. Gym-style RL on top. projectsuperdex. com

Robot hands still break most simulators. Meta Reality Labs just open-sourced Project SuperDex — a contact-first physics engine built for the hard part: multi-finger grasps, in-hand reorientation, soft contact, tactile sensing. VR teleop for cheap demo data. Gym-style RL on top. projectsuperdex. com

Carlos E. Perez

27,627 次观看 • 20 天前

I set up my Elgato Lever to trigger a hydraulic press, which interrupts annoying text-to-speech messages from chat. The tactile feeling is so incredible. This feels like the future of chat punishment.

I set up my Elgato Lever to trigger a hydraulic press, which interrupts annoying text-to-speech messages from chat. The tactile feeling is so incredible. This feels like the future of chat punishment.

shindigs

37,383 次观看 • 5 个月前

I was really impressed by the UMI gripper (Cheng Chi et al.), but a key limitation is that **force-related data wasn’t captured**: humans feel haptic feedback through the mechanical springs, but the robot couldn’t leverage that info, limiting the data’s value for fine-grained manipulation tasks. Led by my amazing students Yolanda Zhu and Binghao Huang, we designed a **portable visuo-tactile gripper** by integrating our dense, flexible tactile arrays with the UMI gripper to enable large-scale in-the-wild data collection. 🔗 We demonstrate **cross-modal representation learning** and **downstream policy learning** on tasks requiring in-hand state estimation (e.g., test tube reorientation) and fine-grained force sensing (e.g., pipette fluid transfer). Key takeaways: - Our flexible tactile arrays store the rich haptic information humans perceive as dense tactile signals. - Portability and robustness are key for in-the-wild data collection; our portable gripper is compact, lightweight, and durable. - Touch provides precise, robust measurements of in-hand object pose, invariant to lighting and viewpoint. - Cross-modal pretraining on large-scale in-the-wild data significantly improves policy robustness and sample efficiency (as shown many times before — and verified again here!). Also check out our previous investigations of dense, flexible tactile grids for understanding human-robot-environment interactions: - Dense tactile glove (Nature ’19): - 3D-ViTac (CoRL ’24):

I was really impressed by the UMI gripper (Cheng Chi et al.), but a key limitation is that force-related data wasn’t captured: humans feel haptic feedback through the mechanical springs, but the robot couldn’t leverage that info, limiting the data’s value for fine-grained manipulation tasks. Led by my amazing students Yolanda Zhu and Binghao Huang, we designed a portable visuo-tactile gripper by integrating our dense, flexible tactile arrays with the UMI gripper to enable large-scale in-the-wild data collection. 🔗 We demonstrate cross-modal representation learning and downstream policy learning on tasks requiring in-hand state estimation (e.g., test tube reorientation) and fine-grained force sensing (e.g., pipette fluid transfer). Key takeaways: - Our flexible tactile arrays store the rich haptic information humans perceive as dense tactile signals. - Portability and robustness are key for in-the-wild data collection; our portable gripper is compact, lightweight, and durable. - Touch provides precise, robust measurements of in-hand object pose, invariant to lighting and viewpoint. - Cross-modal pretraining on large-scale in-the-wild data significantly improves policy robustness and sample efficiency (as shown many times before — and verified again here!). Also check out our previous investigations of dense, flexible tactile grids for understanding human-robot-environment interactions: - Dense tactile glove (Nature ’19): - 3D-ViTac (CoRL ’24):

Yunzhu Li

13,188 次观看 • 1 年前

Students from Brazil built TouchBike, an assistive cycling solution that converts traffic data into tactile feedback. Powered by Huawei Cloud AI, MindSpore, and ModelArts, and using edge computing, it helps hearing-impaired cyclists respond to risks while focusing on the road.

Students from Brazil built TouchBike, an assistive cycling solution that converts traffic data into tactile feedback. Powered by Huawei Cloud AI, MindSpore, and ModelArts, and using edge computing, it helps hearing-impaired cyclists respond to risks while focusing on the road.

Huawei

6,246,642 次观看 • 3 个月前