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One engineering challenge in dexterous Robot hands is balancing strength and speed. Here a SharpaWave performing rapid hand cycles at over 4x/sec. The Dynamic Tactile Array uses visuo-tactile sensing: fingertip integrates camera & 1,000+ tactile pixels.

Rohan Paul

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27,256 views • 3 months ago •via X (Twitter)

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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 views • 10 months ago

Sharp Robotics of Singapore has officially unveiled SharpaWave, an impressively dexterous hand. The 1:1 life-size model boasts 22 degrees of freedom, with over 1,000 tactile sensors per fingertip. It can crack eggs, play the piano, or use scissors, combining strength, speed, and dexterity once thought impossible.

Sharp Robotics of Singapore has officially unveiled SharpaWave, an impressively dexterous hand. The 1:1 life-size model boasts 22 degrees of freedom, with over 1,000 tactile sensors per fingertip. It can crack eggs, play the piano, or use scissors, combining strength, speed, and dexterity once thought impossible.

CyberRobo

893,668 views • 1 year ago

Introducing tactile skin sim-to-real for dexterous in-hand translation! We propose a simulation model for ReSkin, a magnetic tactile sensing skin. It can simulate ternary shear and binary normal forces. More:

Introducing tactile skin sim-to-real for dexterous in-hand translation! We propose a simulation model for ReSkin, a magnetic tactile sensing skin. It can simulate ternary shear and binary normal forces. More:

Haozhi Qi

33,562 views • 2 years ago

The Figure 03 hand has a camera in the palm. It's unclear whether the fingers have tactile sensing.

The Figure 03 hand has a camera in the palm. It's unclear whether the fingers have tactile sensing.

The Humanoid Hub

220,812 views • 1 year ago

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 views • 3 years ago

In-hand cylinder rotation with RL. IROS 2025 Different sizes, zero-shot sim-to-real, and full dynamic tactile feedback — all in one hand. #Sharpa #SharpaWave #IROS2025 #Robotics #DexterousHand #Humanoid #TactileSensing #ReinforcementLearning #IssacSim

In-hand cylinder rotation with RL. IROS 2025 Different sizes, zero-shot sim-to-real, and full dynamic tactile feedback — all in one hand. #Sharpa #SharpaWave #IROS2025 #Robotics #DexterousHand #Humanoid #TactileSensing #ReinforcementLearning #IssacSim

Sharpa

112,645 views • 1 year ago

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 views • 2 years ago

A good hand can push intelligence development. Introducing Eyesight Hand, equipped with full-hand high-res tactile sensors and proprioceptive actuators. It is compliant, agile, and powerful. Good tactile sensing makes learning more efficient and robust. Shout out to Branden!

A good hand can push intelligence development. Introducing Eyesight Hand, equipped with full-hand high-res tactile sensors and proprioceptive actuators. It is compliant, agile, and powerful. Good tactile sensing makes learning more efficient and robust. Shout out to Branden!

Hao-Shu Fang

23,593 views • 1 year ago

Robots need to feel the world to operate in it. Most manipulation policies today are tactile-blind. They either cannot interpret high-frequency tactile signals or treat them as a static channel. And the field lacks enough touch-rich datasets to train tactile-reactive policies at scale. T-Rex was built to answer both. Dantong Niu and team, advised by Jim Fan, Fei-Fei Li, Jitendra MALIK, Pieter Abbeel trevordarrell have tested whether a robot policy can react to high-frequency tactile signals the way human hands do, without giving up the generalization power of modern VLAs. The result: 65% average success rate across 12 real-world tasks. +30 absolute points over the strongest baseline. In one year, tactile VLAs have gone from promising to outperforming non-tactile baselines like pi0.5 on dexterous tasks. #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #TactileSensing #RobotLearning Project link:

Robots need to feel the world to operate in it. Most manipulation policies today are tactile-blind. They either cannot interpret high-frequency tactile signals or treat them as a static channel. And the field lacks enough touch-rich datasets to train tactile-reactive policies at scale. T-Rex was built to answer both. Dantong Niu and team, advised by Jim Fan, Fei-Fei Li, Jitendra MALIK, Pieter Abbeel trevordarrell have tested whether a robot policy can react to high-frequency tactile signals the way human hands do, without giving up the generalization power of modern VLAs. The result: 65% average success rate across 12 real-world tasks. +30 absolute points over the strongest baseline. In one year, tactile VLAs have gone from promising to outperforming non-tactile baselines like pi0.5 on dexterous tasks. #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #TactileSensing #RobotLearning Project link:

Sharpa

12,957 views • 4 months ago

Jitendra Malik rants about parallel-jaw grippers being inadequate; he believes multi-fingered hands with tactile sensing are necessary for advanced dexterous manipulation. Malik is a Professor at UC Berkeley and a Distinguished Scientist at Amazon.

Jitendra Malik rants about parallel-jaw grippers being inadequate; he believes multi-fingered hands with tactile sensing are necessary for advanced dexterous manipulation. Malik is a Professor at UC Berkeley and a Distinguished Scientist at Amazon.

The Humanoid Hub

23,444 views • 8 months ago

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

88,304 views • 9 months ago

I’m gonna’ be honest and say that “Tactile Sensing Season” is here and up next is “Robot Feet Season”🦶🦿 These are the two areas of robotics research that will become more prominent in the coming months. This is the PaXini plantar tactile sole (PX-Footrix). I bought a pair.

I’m gonna’ be honest and say that “Tactile Sensing Season” is here and up next is “Robot Feet Season”🦶🦿 These are the two areas of robotics research that will become more prominent in the coming months. This is the PaXini plantar tactile sole (PX-Footrix). I bought a pair.

Irvin (in Japan 🇯🇵)

65,393 views • 26 days ago

BCI company BrainCo just released a new dexterous hand: Revo3. But this time, it’s not for humans, it’s built for humanoid robots. ＞21 DOF ＞Full-palm + fingertip tactile sensing ＞Direct drive + backdrivable design ＞33 grasp types ＞20N fingertip pinch force ＞Open-source ecosystem, one-click deployment It looks bigger and heavier than the previous versions, but clearly designed for real robot work:picking up objects, using tools, and performing tasks... btw, what do you think are the real differences between the hands of humanoid robots and the bionic hands used by humans?

BCI company BrainCo just released a new dexterous hand: Revo3. But this time, it’s not for humans, it’s built for humanoid robots. ＞21 DOF ＞Full-palm + fingertip tactile sensing ＞Direct drive + backdrivable design ＞33 grasp types ＞20N fingertip pinch force ＞Open-source ecosystem, one-click deployment It looks bigger and heavier than the previous versions, but clearly designed for real robot work:picking up objects, using tools, and performing tasks... btw, what do you think are the real differences between the hands of humanoid robots and the bionic hands used by humans?

CyberRobo

71,948 views • 6 months ago

Presenting research from Berkeley AI Research Humanoid Intelligence Center. DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation.🖐️🤖 Dexterous manipulation requires touch, yet multi-finger tactile data remain scarce and expensive to collect at scale. Through continual vision-to-touch learning, DexTacWAM adapts a pretrained video world model into a visuo-tactile world model for predictive multi-finger contact modeling and action generation. Across six contact-rich dexterous manipulation tasks, DexTacWAM achieves the highest score on every task, averaging 70.6 vs. 38.0 for the strongest baseline. 🔬 Why not simply inject tactile features into the action policy? With the same multi-finger tactile encoder, policy architecture, and training procedure, replacing the tactile world-model latent with direct tactile features drops the 4-task mean from 74.7 to 26.6. Takeaway: the benefit does not come simply from providing tactile observations to the policy, but from making multi-finger contact evolution part of the predicted world state. All code, model weights, and datasets are now open-sourced! 🌐 Project: 📄 Paper: 💻 Code: 🤗 Weights&Data: Author List: Haoran Yuan✈️Corl, Zekai Wang, Boning Shao Haoran Lu, trevordarrell, Ismini Lourentzou, Wei ZHAN

Presenting research from Berkeley AI Research Humanoid Intelligence Center. DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation.🖐️🤖 Dexterous manipulation requires touch, yet multi-finger tactile data remain scarce and expensive to collect at scale. Through continual vision-to-touch learning, DexTacWAM adapts a pretrained video world model into a visuo-tactile world model for predictive multi-finger contact modeling and action generation. Across six contact-rich dexterous manipulation tasks, DexTacWAM achieves the highest score on every task, averaging 70.6 vs. 38.0 for the strongest baseline. 🔬 Why not simply inject tactile features into the action policy? With the same multi-finger tactile encoder, policy architecture, and training procedure, replacing the tactile world-model latent with direct tactile features drops the 4-task mean from 74.7 to 26.6. Takeaway: the benefit does not come simply from providing tactile observations to the policy, but from making multi-finger contact evolution part of the predicted world state. All code, model weights, and datasets are now open-sourced! 🌐 Project: 📄 Paper: 💻 Code: 🤗 Weights&Data: Author List: Haoran Yuan✈️Corl, Zekai Wang, Boning Shao Haoran Lu, trevordarrell, Ismini Lourentzou, Wei ZHAN

Berkeley AI Research

113,791 views • 1 month ago