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Three demos showing SharpaWave’s fine manipulation under high-fidelity teleoperation. Task No.1: putting on a trash bag. With teleoperation and tactile feedback, it finds the rim, opens it, and wraps it neatly — even on a slippery, deformable object. #Sharpa #SharpaWave #Teleoperation #Tactile #DexterousHand

Sharpa

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79,889 görüntüleme • 7 ay önce •via X (Twitter)

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RL-trained in-hand skills, such as grasp stability and object rotation are used to assist teleoperation. Teleoperation data is then used to train a VLA that fuses force and tactile feedback to generate actions. Humanoid hardware: Sharpa Paper:

RL-trained in-hand skills, such as grasp stability and object rotation are used to assist teleoperation. Teleoperation data is then used to train a VLA that fuses force and tactile feedback to generate actions. Humanoid hardware: Sharpa Paper:

The Humanoid Hub

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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

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The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The Humanoid Hub

44,441 görüntüleme • 4 ay önce

Another reason I’m interested in UMI style grippers is that tasks that‘d take 10s for a human take 5x as long with teleoperation. The lack of haptic and force feedback makes it hard to do movements relying on them like here, passing an object from one hand to another. Adding to that: teleoperation means you’re standing a meter away from the objects you’re operating on, and here for example, the box is obstructing the view of the PCBs. Thank you for great teleoperation Felix Neumann 🤝

Another reason I’m interested in UMI style grippers is that tasks that‘d take 10s for a human take 5x as long with teleoperation. The lack of haptic and force feedback makes it hard to do movements relying on them like here, passing an object from one hand to another. Adding to that: teleoperation means you’re standing a meter away from the objects you’re operating on, and here for example, the box is obstructing the view of the PCBs. Thank you for great teleoperation Felix Neumann 🤝

Dominique Paul

15,434 görüntüleme • 5 ay önce

No teleoperation. No simulation. No RL. Multi-fingered robot manipulation policies directly by watching videos of humans with Aria glasses on. It was super fun working on this with Irmak Guzey's lead!! 1/n

No teleoperation. No simulation. No RL. Multi-fingered robot manipulation policies directly by watching videos of humans with Aria glasses on. It was super fun working on this with Irmak Guzey's lead!! 1/n

Homanga Bharadhwaj

51,532 görüntüleme • 7 ay önce

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

87,951 görüntüleme • 6 ay önce

With RL, the robot can learn very precise tasks, like fastening a zip tie, and can actually do it more consistently and more quickly than even human teleoperation.

With RL, the robot can learn very precise tasks, like fastening a zip tie, and can actually do it more consistently and more quickly than even human teleoperation.

Physical Intelligence

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Boston Dynamics’ robot-behavior team lead highlights three core initiatives aimed at advancing Atlas’s dexterity: ⦿ Reinforcement learning in simulation ⦿ Whole-body teleoperation to collect data for imitation learning ⦿ Tactile sensing grippers

Boston Dynamics’ robot-behavior team lead highlights three core initiatives aimed at advancing Atlas’s dexterity: ⦿ Reinforcement learning in simulation ⦿ Whole-body teleoperation to collect data for imitation learning ⦿ Tactile sensing grippers

The Humanoid Hub

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Embodied Avatar: Full-body Teleoperation Platform🥳 Everyone has fantasized about having an embodied avatar! Full-body teleoperation and full-body data acquisition platform is waiting for you to try it out!

Unitree

26,064,483 görüntüleme • 8 ay önce

Wouldn't it be great if we could train robots without any teleoperation! In our latest paper, we train robots to mimic a human video of the task by simply matching the object features using RL. We only need one video and under an hour of robot training.

Wouldn't it be great if we could train robots without any teleoperation! In our latest paper, we train robots to mimic a human video of the task by simply matching the object features using RL. We only need one video and under an hour of robot training.

Lerrel Pinto

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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 görüntüleme • 1 yıl önce

Wuji Glove × Wuji Hand｜Teleoperation 1:1 motion mapping. Low-latency response. 7 dexterous motions — witness the seamless choreography of Wuji Glove × Wuji Hand. Learn more: 🔗 📄 Product Docs: 🛒 Sales Inquiries: sales@wuji.tech 🎯 Careers: talentpro@wuji.tech #wujitech #wujihand #wujiglove #Robotics #DexterousHand #Teleoperation #EmbodiedAI #PhysicalAI

Wuji Glove × Wuji Hand｜Teleoperation 1:1 motion mapping. Low-latency response. 7 dexterous motions — witness the seamless choreography of Wuji Glove × Wuji Hand. Learn more: 🔗 📄 Product Docs: 🛒 Sales Inquiries: [email protected] 🎯 Careers: [email protected] #wujitech #wujihand #wujiglove #Robotics #DexterousHand #Teleoperation #EmbodiedAI #PhysicalAI

WUJI TECH

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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 görüntüleme • 1 ay önce

Sharpa Robotics officially launched its dexterous hand, the SharpaWave, on October 16, moving it into mass production. The hand, which impressed many at ICRA 2025, is built around what the company calls a "Dynamic Tactile Array" (DTA) for high-fidelity sensing. Key specs from the official brochure are noteworthy: • Human-like design: 22 degrees of freedom in a 1:1 human-sized form factor. • Advanced sensing: The DTA features >1000 tactile pixels per fingertip, 20 N of fingertip strength with a movement speed of >4 Hz across all gestures. With out-of-the-box support for ROS and MuJoCo simulation, Sharpa is making a clear play for the robotics R&D community.

Sharpa Robotics officially launched its dexterous hand, the SharpaWave, on October 16, moving it into mass production. The hand, which impressed many at ICRA 2025, is built around what the company calls a "Dynamic Tactile Array" (DTA) for high-fidelity sensing. Key specs from the official brochure are noteworthy: • Human-like design: 22 degrees of freedom in a 1:1 human-sized form factor. • Advanced sensing: The DTA features >1000 tactile pixels per fingertip, 20 N of fingertip strength with a movement speed of >4 Hz across all gestures. With out-of-the-box support for ROS and MuJoCo simulation, Sharpa is making a clear play for the robotics R&D community.

Humanoids daily

66,083 görüntüleme • 8 ay önce

Unitree's open-source teleoperation in action, with 3-fingered 'Dex3-1' hands and a camera mounted to each wrist.

Unitree's open-source teleoperation in action, with 3-fingered 'Dex3-1' hands and a camera mounted to each wrist.

The Humanoid Hub

24,697 görüntüleme • 1 yıl önce

NVIDIA has published a paper on DREAMGEN – a powerful 4-step pipeline for generating synthetic data for humanoids that enables task and environment generalization. - Step 1: Fine-tune a video generation model using a small number of human teleoperation videos - Step 2: Prompt the fine-tuned model to turn a single real image into new AI-imagined videos - Step 3: Automatically label actions in the generated videos - Step 4: Train a robot AI model with the labeled synthetic dataset This enabled humanoid robots to perform 22 novel behaviors – such as pouring, opening/closing articulated objects, and manipulating a variety of tools. The original teleoperation dataset only included pick-and-place tasks. This takes task extensibility to another level without requiring human teleoperation for every single task. The pipeline will be made open-source soon. Project page:

NVIDIA has published a paper on DREAMGEN – a powerful 4-step pipeline for generating synthetic data for humanoids that enables task and environment generalization. - Step 1: Fine-tune a video generation model using a small number of human teleoperation videos - Step 2: Prompt the fine-tuned model to turn a single real image into new AI-imagined videos - Step 3: Automatically label actions in the generated videos - Step 4: Train a robot AI model with the labeled synthetic dataset This enabled humanoid robots to perform 22 novel behaviors – such as pouring, opening/closing articulated objects, and manipulating a variety of tools. The original teleoperation dataset only included pick-and-place tasks. This takes task extensibility to another level without requiring human teleoperation for every single task. The pipeline will be made open-source soon. Project page:

The Humanoid Hub

12,074 görüntüleme • 1 yıl önce

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 görüntüleme • 6 ay önce

Two Reachy 2 setting and clearing the table, all in real time teleoperation! Shot in a single take with all the successes… and a small fail👀 One example of what Reachy 2 can do: efficient, versatile object manipulation, with the precision needed for delicate or fragile tasks

Two Reachy 2 setting and clearing the table, all in real time teleoperation! Shot in a single take with all the successes… and a small fail👀 One example of what Reachy 2 can do: efficient, versatile object manipulation, with the precision needed for delicate or fragile tasks

Pollen Robotics

27,200 görüntüleme • 10 ay önce

[Open Source] Unitree First View Teleoperation for Humanoid Robots In order to advance the convenience of data collection for humanoid robots, we refer to other solutions to do the adaptation development and open source. Github： #Unitree #Humanoid #AGI #AI #EmbodiedIntelligence #Manipulation #Teleoperation #DataCollection

[Open Source] Unitree First View Teleoperation for Humanoid Robots In order to advance the convenience of data collection for humanoid robots, we refer to other solutions to do the adaptation development and open source. Github： #Unitree #Humanoid #AGI #AI #EmbodiedIntelligence #Manipulation #Teleoperation #DataCollection

Unitree

116,565 görüntüleme • 1 yıl önce

A high-quality haptic control for robots as a weekend project? All for under $600. [📍 Save Github & arXiv for later] A team built DOGlove, an open-source haptic glove for robot teleoperation that you can assemble yourself for under $600. It is built for real control, not demos. • Tracks 21 degrees of freedom with a custom joint design • Provides cable-driven force feedback plus fingertip haptics • Can be used to collect data for imitation learning Affordable haptics could make teleoperation more reliable and improve the quality of data we use to train physical AI. Project link: Credit: DOGlove team, shared by Han Zhang —- Weekly robotics and AI insights. Subscribe free:

A high-quality haptic control for robots as a weekend project? All for under $600. [📍 Save Github & arXiv for later] A team built DOGlove, an open-source haptic glove for robot teleoperation that you can assemble yourself for under $600. It is built for real control, not demos. • Tracks 21 degrees of freedom with a custom joint design • Provides cable-driven force feedback plus fingertip haptics • Can be used to collect data for imitation learning Affordable haptics could make teleoperation more reliable and improve the quality of data we use to train physical AI. Project link: Credit: DOGlove team, shared by Han Zhang —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

18,290 görüntüleme • 5 ay önce