Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

At #CVPR2024: Tactile-augmented Radiance Fields! We probe a scene with a touch sensor and localize each sample within a NeRF. We use diffusion to estimate the tactile signals for the points we didn't touch. w/ Yiming Dou, Antonio Loquercio, Fengyu Yang, Yi Liu

Andrew Owens

2,376 subscribers

38,611 просмотров • 2 лет назад •via X (Twitter)

Здоровье и велнес Наука и технологии Образование #CVPR2024

Anya Rossi• Live Now

Private livecam show

Комментарии: 8

Фото профиля Andrew Owens

Andrew Owens2 лет назад

One fun technical detail: we mount the touch sensor to an RGB-D camera using a selfie stick. Since "vision-based" touch sensors (like DIGIT, GelSight) are based on ordinary cameras, you can use multi-view geometry to estimate the relative pose between both sensors!

Фото профиля Andrew Owens

Andrew Owens2 лет назад

Here's what the capturing procedure looks like.

Фото профиля Andrew Owens

Andrew Owens2 лет назад

Project page: Paper:

Фото профиля Andrew Owens

Andrew Owens2 лет назад

The idea of filling in a touch signal using a generative model is similar to recent work by @ShaohongZhong et al.: which uses robotic proprioception and GANs for object-scale reconstructions.

Фото профиля Yiming Dou

Yiming Dou2 лет назад

@antoniloq Thanks Andrew! This project wouldn’t be possible without you advising on every single detail!😀

Фото профиля Igor Gilitschenski

Igor Gilitschenski2 лет назад

@_YimingDou @antoniloq That is amazing work! Congrats everyone!

Фото профиля Andrew Owens

Andrew Owens2 лет назад

@_YimingDou @antoniloq Thanks so much, Igor!

Фото профиля Mustafa

Mustafa2 лет назад

@_YimingDou @antoniloq Let's chat. Unfortunately can't dm you

Похожие видео

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

453,035 просмотров • 1 год назад

Can we bring human-like Touch to robots🤖? Introducing our CoRL work on 3D-ViTac. Humans rely on both vision 👁️ and touch 🫳 for complex tasks. With combined visual-tactile sensing, robots can now tackle challenging tasks, like precise in-hand reorientation, fragile objects grasping. Website: #Robotics #CoRL2024 #Touch #tactile #AI #ML

Can we bring human-like Touch to robots🤖? Introducing our CoRL work on 3D-ViTac. Humans rely on both vision 👁️ and touch 🫳 for complex tasks. With combined visual-tactile sensing, robots can now tackle challenging tasks, like precise in-hand reorientation, fragile objects grasping. Website: #Robotics #CoRL2024 #Touch #tactile #AI #ML

Binghao Huang

49,500 просмотров • 1 год назад

We just released TAVI -- a robotics framework that combines touch and vision to solve challenging dexterous tasks in under 1 hour. The key? Use human demonstrations to initialize a policy, followed by tactile-based online learning with vision-based rewards. Details in🧵(1/7)

We just released TAVI -- a robotics framework that combines touch and vision to solve challenging dexterous tasks in under 1 hour. The key? Use human demonstrations to initialize a policy, followed by tactile-based online learning with vision-based rewards. Details in🧵(1/7)

Lerrel Pinto

138,536 просмотров • 2 лет назад

(1/2) Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation #ECCV2024! We show a theoretical derivation to create radiance fields directly from meshes. Thus, we can obtain GT training data for generative NeRF methods.

(1/2) Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation #ECCV2024! We show a theoretical derivation to create radiance fields directly from meshes. Thus, we can obtain GT training data for generative NeRF methods.

Matthias Niessner

16,833 просмотров • 1 год назад

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 просмотров • 5 месяцев назад

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 ⬇️

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

100,151 просмотров • 3 лет назад

Progressively Optimized Local Radiance Fields for Robust View Synthesis paper page: present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the Tanks and Temples dataset and our collected outdoor dataset, Static Hikes, show that our approach compares favorably with the state-of-the-art.

Progressively Optimized Local Radiance Fields for Robust View Synthesis paper page: present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the Tanks and Temples dataset and our collected outdoor dataset, Static Hikes, show that our approach compares favorably with the state-of-the-art.

AK

140,616 просмотров • 3 лет назад

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

14,772 просмотров • 12 дней назад

Making basketball more accessible. OneCourt’s tactile broadcast devices use vibration and audio to help blind and low-vision fans experience Bucks games through touch. Together with Ticketmaster and onecourt_io, we are proud to bring these devices to Fiserv Forum. Reserve yours now:

Making basketball more accessible. OneCourt’s tactile broadcast devices use vibration and audio to help blind and low-vision fans experience Bucks games through touch. Together with Ticketmaster and onecourt_io, we are proud to bring these devices to Fiserv Forum. Reserve yours now:

Milwaukee Bucks

13,602 просмотров • 3 месяцев назад

In house tactile sensor prototype. Detects taps, motion, and multiple contact with force magnitudes. Built to be cheap, robust, and replaceable. Not in our demos yet, still WIP. We pair simple tactile sensing with torque transparent joints to recover rich contact without expensive sensors.

In house tactile sensor prototype. Detects taps, motion, and multiple contact with force magnitudes. Built to be cheap, robust, and replaceable. Not in our demos yet, still WIP. We pair simple tactile sensing with torque transparent joints to recover rich contact without expensive sensors.

Kyber Labs

38,930 просмотров • 2 месяцев назад

ViTacFormer is a unified visuo-tactile framework for dexterous robot manipulation. It fuses high-res visual+tactile data using cross-attention and predicts future tactile signals via an autoregressive head, enabling multi-fingered hands to perform precise, long-horizon tasks.

ViTacFormer is a unified visuo-tactile framework for dexterous robot manipulation. It fuses high-res visual+tactile data using cross-attention and predicts future tactile signals via an autoregressive head, enabling multi-fingered hands to perform precise, long-horizon tasks.

The Humanoid Hub

10,816 просмотров • 11 месяцев назад

A lot of hype around giving robots touch. That's the OG tactile sensing. FORCE. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

A lot of hype around giving robots touch. That's the OG tactile sensing. FORCE. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

215,973 просмотров • 14 дней назад

Tactile sensing is gaining traction, but slowly. Why? Because integration remains difficult. But what if adding touch sensors to your robot was as easy as hitting “print”? Introducing eFlesh: a 3D-printable, customizable tactile sensor. Shape it. Size it. Print it. 🧶👇

Tactile sensing is gaining traction, but slowly. Why? Because integration remains difficult. But what if adding touch sensors to your robot was as easy as hitting “print”? Introducing eFlesh: a 3D-printable, customizable tactile sensor. Shape it. Size it. Print it. 🧶👇

Raunaq Bhirangi

85,757 просмотров • 1 год назад

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

AK

62,768 просмотров • 3 лет назад

👽 There was a scene when we filming, his heart was pounding very fast. In fact I didn't use my hands to touch it. I use something else to touch… Wait! Huh?! 😭 #รักใต้บรรทัดEP1 #BattleOfTheWriters1STLine #TutorYim #TutorKrp #Mynameis_yim

Sensitive content

👽 There was a scene when we filming, his heart was pounding very fast. In fact I didn't use my hands to touch it. I use something else to touch… Wait! Huh?! 😭 #รักใต้บรรทัดEP1 #BattleOfTheWriters1STLine #TutorYim #TutorKrp #Mynameis_yim

EL💙🌌🩵 Ꮚ~Slow (Very Busy)

11,804 просмотров • 1 год назад

Teaching robots to learn only from RGB human videos is hard! In Feel The Force (FTF), we teach robots to mimic the tactile feedback humans experience when handling objects. This allows for delicate, touch-sensitive tasks—like picking up a raw egg without breaking it. 🧵👇

Teaching robots to learn only from RGB human videos is hard! In Feel The Force (FTF), we teach robots to mimic the tactile feedback humans experience when handling objects. This allows for delicate, touch-sensitive tasks—like picking up a raw egg without breaking it. 🧵👇

Lerrel Pinto

70,117 просмотров • 1 год назад

🤖🤖🤖 Following RoboVerse, we introduce another work focused on Robotic Tactile Simulation - Taccel Simulator. Taccel is a high-performance simulation platform for vision-based tactile sensors and robots. 🚀🚀🚀 Boosted by Nvidia Warp, we optimize Taccel with highly parallelized simulations and support 900fps simulation with 4k+ parallel training envs. 🤝🤝🤝 Taccel is designed with user-friendly APIs and is easy to use. We open-sourced all the code and documentation. Feel free to try! Project: Preprint: Code:

🤖🤖🤖 Following RoboVerse, we introduce another work focused on Robotic Tactile Simulation - Taccel Simulator. Taccel is a high-performance simulation platform for vision-based tactile sensors and robots. 🚀🚀🚀 Boosted by Nvidia Warp, we optimize Taccel with highly parallelized simulations and support 900fps simulation with 4k+ parallel training envs. 🤝🤝🤝 Taccel is designed with user-friendly APIs and is easy to use. We open-sourced all the code and documentation. Feel free to try! Project: Preprint: Code:

Siyuan Huang

10,650 просмотров • 1 год назад

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 просмотров • 11 месяцев назад

Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! [📍It’s open source!] A new tactile sensor, called e-Flesh, with a simple working principle… measure deformations in 3D printable microstructures. Since e-Flesh is 3D printable, you can make it in all shapes and sizes for applications ranging from foot fall sensing to multifingered hands. This is critical in getting touch not just on fingertips, but all around robots. eFlesh can democratize touch sensing with open-sourced❗️ Make your own: Paper:

Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! [📍It’s open source!] A new tactile sensor, called e-Flesh, with a simple working principle… measure deformations in 3D printable microstructures. Since e-Flesh is 3D printable, you can make it in all shapes and sizes for applications ranging from foot fall sensing to multifingered hands. This is critical in getting touch not just on fingertips, but all around robots. eFlesh can democratize touch sensing with open-sourced❗️ Make your own: Paper:

Ilir Aliu

154,765 просмотров • 4 месяцев назад