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SharpaWave, a 22-DOF dexterous hand, by Singapore-based startup Sharpa. ⦿ Features over 1,000 tactile pixels per fingertip and 0.005 N pressure sensitivity. ⦿ Delivers 20 N fingertip strength and more than 4 Hz hand movement speed across all gestures.

The Humanoid Hub

64,035 subscribers

72,041 views • 1 year ago •via X (Twitter)

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4 Comments

Robert Scoble1 year ago

This is a real breakthrough that will lead to many new use cases, like turning an inside shirt out.

AndaSeat1 year ago

🎯 Holds breath in sniper mode... 🎮 X-Air Pro's clutch features: 💨 Breathable mesh for sweaty 1v5 moments 🔫 Perfect height for that winning headshot ⚡ 5D armrests for precise aim control 😅 "Why did I solo queue" comfort mode 🎯 For those who main AWP and live dangerously! 🪑✨ Aim better: 🎪 Headshot deal: Snipe $20 off! #AndaSeat #CSGO #Valorant #ESports #Gaming

Brian Sowards (he/they)1 year ago

At least use their handle

Lauri Nurmi1 year ago

can they cover it with artificial skin?

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Singapore's Sharpa unveiled SharpaWave, a lifelike robotic hand —Features 22 DOF to balance for dexterity and strength —Each fingertip has 1,000+ tactile sensing pixels and 5 mN pressure sensitivity —AI models adapt the hand's grip and modulate force

Singapore's Sharpa unveiled SharpaWave, a lifelike robotic hand —Features 22 DOF to balance for dexterity and strength —Each fingertip has 1,000+ tactile sensing pixels and 5 mN pressure sensitivity —AI models adapt the hand's grip and modulate force

Brett Adcock

12,418 views • 1 year ago

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.

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

27,256 views • 1 month ago

Most humanlike robotic hand ever built❓ Developed by Sharpa in Singapore, it’s a 1:1 life-size, 22-DoF robotic hand designed for both precision and strength! Capable of manipulating fragile objects like an egg or applying 30 N of fingertip force without damage. ✅ Over 1,000 tactile pixels per fingertip with 0.005 N sensitivity ✅ Dynamic Tactile Array operating at 180 FPS for real-time feedback ✅ Certified for 1 million grip cycles without failure ✅ Ethernet interface, ROS and MuJoCo compatible This isn’t a lab prototype. Sharpa began mass production and customer shipments in October 2025. Full details: —- Weekly robotics and AI insights. Subscribe free:

Most humanlike robotic hand ever built❓ Developed by Sharpa in Singapore, it’s a 1:1 life-size, 22-DoF robotic hand designed for both precision and strength! Capable of manipulating fragile objects like an egg or applying 30 N of fingertip force without damage. ✅ Over 1,000 tactile pixels per fingertip with 0.005 N sensitivity ✅ Dynamic Tactile Array operating at 180 FPS for real-time feedback ✅ Certified for 1 million grip cycles without failure ✅ Ethernet interface, ROS and MuJoCo compatible This isn’t a lab prototype. Sharpa began mass production and customer shipments in October 2025. Full details: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu - eu/acc

23,360 views • 9 months ago

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 views • 9 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

893,446 views • 9 months ago

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 • 7 months ago

Wuji Hand by Chinese company Pan Motor. Features 20 active DOFs (4 per finger), weighs <600g, 15N fingertip force, 10kg static grasp, >300,000-cycle lifespan, and 30 arcminute accuracy.

Wuji Hand by Chinese company Pan Motor. Features 20 active DOFs (4 per finger), weighs <600g, 15N fingertip force, 10kg static grasp, >300,000-cycle lifespan, and 30 arcminute accuracy.

The Humanoid Hub

115,943 views • 10 months ago

ROBOTIS just upgraded its wheeled humanoid with the HX5-D20 dexterous hand 👋 •»20 DOF fully active,5 fingers × 4 DOF each. Human-like motion, from piano keys to scissors. • »Direct drive, wire-free ,Powered by ultra-small Dynamixel actuators. Zero backlash. High durability. Clean control. • »Fingertip tactile sensing ,Real-time pressure feedback for force-controlled grasping, blind operation, and fragile object handling. Already deployed at Google, Apple, and MIT labs.

ROBOTIS just upgraded its wheeled humanoid with the HX5-D20 dexterous hand 👋 •»20 DOF fully active,5 fingers × 4 DOF each. Human-like motion, from piano keys to scissors. • »Direct drive, wire-free ,Powered by ultra-small Dynamixel actuators. Zero backlash. High durability. Clean control. • »Fingertip tactile sensing ,Real-time pressure feedback for force-controlled grasping, blind operation, and fragile object handling. Already deployed at Google, Apple, and MIT labs.

CyberRobo

42,010 views • 5 months ago

South Korean company WIRobotics unveiled ALLEX, a general-purpose humanoid robot with low-friction backdrivable arms and human-like hands. Each hand features 15 DOF, supports lifting over 30 kg, and delivers more than 40 N at the fingertips.

South Korean company WIRobotics unveiled ALLEX, a general-purpose humanoid robot with low-friction backdrivable arms and human-like hands. Each hand features 15 DOF, supports lifting over 30 kg, and delivers more than 40 N at the fingertips.

The Humanoid Hub

195,504 views • 11 months 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

71,783 views • 4 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

87,951 views • 7 months ago

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 →

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

38,094 views • 4 months ago

Impressive on every dimension! Genesis AI has announced GENE-26.5, its first robotic foundation model, aimed at achieving human-level dexterous manipulation: - Single model, shared weights, handles egg cracking, lab pipetting, bimanual Rubik's Cube, smoothie making, and wire harnessing. Most tasks: under 1 hour of task-specific data, 1x real-world speed. - Genesis Hand 1.0: human-size, 20 active DoF, soft-contact skin. Paired glove gives a 1:1:1 mapping (glove, human hand, robot hand). 100x cheaper than teleop, 5x more data-efficient internally. - Data engine pulls from three sources: glove data, egocentric (head-cam) video, and large-scale internet video. The 1:1 hand-to-human match closes the embodiment gap, letting Genesis use video data more effectively than rivals. - Flow matching across vision, tactile, proprioception, and language inputs. Per Genesis, scaling data and compute directly improves zero-shot

Impressive on every dimension! Genesis AI has announced GENE-26.5, its first robotic foundation model, aimed at achieving human-level dexterous manipulation: - Single model, shared weights, handles egg cracking, lab pipetting, bimanual Rubik's Cube, smoothie making, and wire harnessing. Most tasks: under 1 hour of task-specific data, 1x real-world speed. - Genesis Hand 1.0: human-size, 20 active DoF, soft-contact skin. Paired glove gives a 1:1:1 mapping (glove, human hand, robot hand). 100x cheaper than teleop, 5x more data-efficient internally. - Data engine pulls from three sources: glove data, egocentric (head-cam) video, and large-scale internet video. The 1:1 hand-to-human match closes the embodiment gap, letting Genesis use video data more effectively than rivals. - Flow matching across vision, tactile, proprioception, and language inputs. Per Genesis, scaling data and compute directly improves zero-shot

The Humanoid Hub

95,691 views • 3 months ago

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

453,260 views • 1 year ago

Can robotic hands really outperform human dexterity? Meet the Linker Hand L30 from LinkerBot. This tendon-driven dexterous hand packs high precision and compact size, making it a serious contender for complex manipulation tasks: ► 22 Degrees of Freedom utilizing a sophisticated tendon drive system. ► Ultra-fine positioning control with ±0.20mm repeatable accuracy. ► Rapid response speed, achieving up to 450°/S for core movements. ► Adaptive grasping enabled by high-sensitivity tactile sensors built into the fingertips. ► Compact dimensions (85mm average width, 179mm length) despite its complex architecture.

Can robotic hands really outperform human dexterity? Meet the Linker Hand L30 from LinkerBot. This tendon-driven dexterous hand packs high precision and compact size, making it a serious contender for complex manipulation tasks: ► 22 Degrees of Freedom utilizing a sophisticated tendon drive system. ► Ultra-fine positioning control with ±0.20mm repeatable accuracy. ► Rapid response speed, achieving up to 450°/S for core movements. ► Adaptive grasping enabled by high-sensitivity tactile sensors built into the fingertips. ► Compact dimensions (85mm average width, 179mm length) despite its complex architecture.

RoboHub🤖

22,556 views • 10 months ago

Genesis AI has stepped into the spotlight The company is a SF Bay Area-based full-stack embodied AI startup. The Stack - Foundation model - 20 DoF dexterous hand - Tactile-sensing data-collection glove + Data engine - Custom motor controllers - In-house high-fidelity simulator The Thesis: Manipulation is a system problem, not just an AI problem. The company’s first general-purpose robot is teased as the “ultimate culmination” of the stack. Founders Zhou Xian (CEO) and Théophile Gervet (President), formerly of Mistral, NVIDIA, Google, CMU, MIT, and Stanford. $105 million raised in July 2025. Dual headquarters in San Carlos, CA and Paris.

Genesis AI has stepped into the spotlight The company is a SF Bay Area-based full-stack embodied AI startup. The Stack - Foundation model - 20 DoF dexterous hand - Tactile-sensing data-collection glove + Data engine - Custom motor controllers - In-house high-fidelity simulator The Thesis: Manipulation is a system problem, not just an AI problem. The company’s first general-purpose robot is teased as the “ultimate culmination” of the stack. Founders Zhou Xian (CEO) and Théophile Gervet (President), formerly of Mistral, NVIDIA, Google, CMU, MIT, and Stanford. $105 million raised in July 2025. Dual headquarters in San Carlos, CA and Paris.

The Humanoid Hub

50,111 views • 3 months ago

Open-Source Robotic Hand That Costs Just $300. California-based company TetherIA.ai has officially launched its open-source robotic hand, the Aero Hand Open. Designed to be the most capable at the lowest cost, this hand is a new tool for embodied AI research. In the clip, it demonstrates impressive dexterity by grasping an M5 screw, picking up an iPhone, and opening a soda can. Key Features: ► Price: $314.00 ► Open Source: CAD, electronics, and firmware are all open source. ► Actuation: 7 Active Degrees of Freedom (16 joints) with a tendon-driven design. ► Weight: Under 400 grams. ► Purpose: Built for embodied AI research to make dexterous manipulation accessible to all. For more details and to purchase:

Open-Source Robotic Hand That Costs Just $300. California-based company TetherIA.ai has officially launched its open-source robotic hand, the Aero Hand Open. Designed to be the most capable at the lowest cost, this hand is a new tool for embodied AI research. In the clip, it demonstrates impressive dexterity by grasping an M5 screw, picking up an iPhone, and opening a soda can. Key Features: ► Price: $314.00 ► Open Source: CAD, electronics, and firmware are all open source. ► Actuation: 7 Active Degrees of Freedom (16 joints) with a tendon-driven design. ► Weight: Under 400 grams. ► Purpose: Built for embodied AI research to make dexterous manipulation accessible to all. For more details and to purchase:

RoboHub🤖

45,830 views • 10 months ago

We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate. Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution. Our recipe is called "EgoScale": - Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks. - Most surprising result: a *single* teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency. - Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone. The scalable path to robot dexterity was never more robots. It was always us. Deep dives in thread:

We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate. Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution. Our recipe is called "EgoScale": - Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks. - Most surprising result: a single teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency. - Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone. The scalable path to robot dexterity was never more robots. It was always us. Deep dives in thread:

Jim Fan

294,395 views • 5 months ago

A new dexterous hand is here. DexcelRobotics, a startup founded by a former core member of Tencent Robotics X, has launched its first product, the Apex Hand. The company claims it's the first in the industry capable of operating a cell phone with a single hand. The Apex Hand is a well-rounded performer, with a focus on real-world application. Here are its key specs: ► Degrees of Freedom: 21, which the founder notes is enough to replicate most human hand functions. ► Strength: A single-finger force of ~2.5kg and a vertical lifting capacity of ~30kg. ► Speed & Precision: A response time near human-level, with positioning accuracy of ≤0.1mm. ► Robustness: Can withstand unexpected impacts and maintain stability. ► Tactile: Features self-developed flexible electronic skin with a sub-millisecond communication delay and >1000Hz refresh rate. The founder explains that while academic research often focuses on single-point breakthroughs, a commercial product needs hardware stability, data handling, and model capabilities. The company’s full-stack development experience and modular design are key to bringing this technology to a broader market, starting with semi-structured industrial environments before reaching homes.

A new dexterous hand is here. DexcelRobotics, a startup founded by a former core member of Tencent Robotics X, has launched its first product, the Apex Hand. The company claims it's the first in the industry capable of operating a cell phone with a single hand. The Apex Hand is a well-rounded performer, with a focus on real-world application. Here are its key specs: ► Degrees of Freedom: 21, which the founder notes is enough to replicate most human hand functions. ► Strength: A single-finger force of ~2.5kg and a vertical lifting capacity of ~30kg. ► Speed & Precision: A response time near human-level, with positioning accuracy of ≤0.1mm. ► Robustness: Can withstand unexpected impacts and maintain stability. ► Tactile: Features self-developed flexible electronic skin with a sub-millisecond communication delay and >1000Hz refresh rate. The founder explains that while academic research often focuses on single-point breakthroughs, a commercial product needs hardware stability, data handling, and model capabilities. The company’s full-stack development experience and modular design are key to bringing this technology to a broader market, starting with semi-structured industrial environments before reaching homes.

RoboHub🤖

125,293 views • 10 months ago

NEW ROBOT BENCHMARK: If your robot can do Origami, it can do anything! Called The Robotic Origami Challenge, it is a dexterous-manipulation competition and benchmark held at IROS 2026, organized by 13 co-organizers with the Nippon Origami Association as judge and task curator -> pretty cool to have them on board imho. The evaluation consists of single task: a traditional Japanese paper airplane, in exactly six folds, from a 15×15 cm sheet of ≥60 gsm paper, on a competition-supplied standardized rig (bimanual arms + Sharpa Hands), both remotely and on-site. Teams bring policies, not hardware. An "Origami Grand Master" declares pass/fail on crease accuracy, structural fidelity, symmetry and paper integrity. Among passes, faster folds rank higher, with a 10-minute-per-attempt ceiling and flight explicitly irrelevant to the score. -> I find it interesting how they chose to evaluate the task. Quality is a binary pass or fail, therefore speed becomes the only thing graded here. Speed is currently the bottleneck in dexterous manipulation though, so this choice makes sense. I wonder whether there could be finer ways to grade the qaulity of such a creative task though. When it comes to data, registered teams get 500+ teleoperation episodes (six camera streams, 65-D joint state/action, 10-fingertip 6-axis tactile), an NVIDIA Isaac Sim environment with thin-shell paper physics (plastic creasing + fold memory), digital twins of every partner hand, and a remote eval lab (upload a policy, queue an eval, get scored). Still, I think it is a great dexterity benchmark the field badly needs, it supplies the hardware, an outside human judges, and the pass criterion is externally defined -> all three degrees of freedom are checked! Neutral measurement layer, here we go! The task is engineered to be un-gameable and to isolate pure dexterity. A known figure, exactly six folds, judged on creases, with flight explicitly declared irrelevant (the latter makes sense to me). Therefore, this underlines the goal to focus on dexterity, not task-selection or other strategies. I really like origami as an ideal controlled dexterity task: deterministic goal, deformable medium, sequential, bimanual, precision-bound. I am just not quite satisfied again by the binary pass or fail, I think quality of execution could be finely graded! But again, I understand this is not the goal yet. Also interesting to see the Sharpa Hands as de facto standard for everyone. Total land-grab that anoints Sharpa as the reference dexterous hand, also featured in Google's Gemini Robotics 2. By providing the hardware, the benchmark measures software while quietly making "good on Sharpa" the definition of good, and Sharpa gets real world data and feedback as a bonus. That's smart, the data flywheel starts spinning. The provided dataset is the richest tactile-manipulation corpus I have seen yet: 10-fingertip 6-axis tactile, plus plastic creasing and fold memory. 500+ teleop episodes with six camera streams, 65-D joint state/action, and ten fingertip 6-axis tactile sensors. The force/tactile channel are parts of the the benchmark's data, this is the first time I see this. Credits where it's due: organizers include Yang Gao, Noriaki Hirose, Steve Xie, Chris Paxton, Jiafei Duan, Michael Cho - Rbt/Acc, Michael Yuan, Haoquan Fang, and others.

NEW ROBOT BENCHMARK: If your robot can do Origami, it can do anything! Called The Robotic Origami Challenge, it is a dexterous-manipulation competition and benchmark held at IROS 2026, organized by 13 co-organizers with the Nippon Origami Association as judge and task curator -> pretty cool to have them on board imho. The evaluation consists of single task: a traditional Japanese paper airplane, in exactly six folds, from a 15×15 cm sheet of ≥60 gsm paper, on a competition-supplied standardized rig (bimanual arms + Sharpa Hands), both remotely and on-site. Teams bring policies, not hardware. An "Origami Grand Master" declares pass/fail on crease accuracy, structural fidelity, symmetry and paper integrity. Among passes, faster folds rank higher, with a 10-minute-per-attempt ceiling and flight explicitly irrelevant to the score. -> I find it interesting how they chose to evaluate the task. Quality is a binary pass or fail, therefore speed becomes the only thing graded here. Speed is currently the bottleneck in dexterous manipulation though, so this choice makes sense. I wonder whether there could be finer ways to grade the qaulity of such a creative task though. When it comes to data, registered teams get 500+ teleoperation episodes (six camera streams, 65-D joint state/action, 10-fingertip 6-axis tactile), an NVIDIA Isaac Sim environment with thin-shell paper physics (plastic creasing + fold memory), digital twins of every partner hand, and a remote eval lab (upload a policy, queue an eval, get scored). Still, I think it is a great dexterity benchmark the field badly needs, it supplies the hardware, an outside human judges, and the pass criterion is externally defined -> all three degrees of freedom are checked! Neutral measurement layer, here we go! The task is engineered to be un-gameable and to isolate pure dexterity. A known figure, exactly six folds, judged on creases, with flight explicitly declared irrelevant (the latter makes sense to me). Therefore, this underlines the goal to focus on dexterity, not task-selection or other strategies. I really like origami as an ideal controlled dexterity task: deterministic goal, deformable medium, sequential, bimanual, precision-bound. I am just not quite satisfied again by the binary pass or fail, I think quality of execution could be finely graded! But again, I understand this is not the goal yet. Also interesting to see the Sharpa Hands as de facto standard for everyone. Total land-grab that anoints Sharpa as the reference dexterous hand, also featured in Google's Gemini Robotics 2. By providing the hardware, the benchmark measures software while quietly making "good on Sharpa" the definition of good, and Sharpa gets real world data and feedback as a bonus. That's smart, the data flywheel starts spinning. The provided dataset is the richest tactile-manipulation corpus I have seen yet: 10-fingertip 6-axis tactile, plus plastic creasing and fold memory. 500+ teleop episodes with six camera streams, 65-D joint state/action, and ten fingertip 6-axis tactile sensors. The force/tactile channel are parts of the the benchmark's data, this is the first time I see this. Credits where it's due: organizers include Yang Gao, Noriaki Hirose, Steve Xie, Chris Paxton, Jiafei Duan, Michael Cho - Rbt/Acc, Michael Yuan, Haoquan Fang, and others.

Léo

22,827 views • 1 day ago