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Despite great advances in learning dexterity, hardware remains a major bottleneck. Most dexterous hands are either bulky, weak or expensive. I’m thrilled to present the RUKA Hand — a powerful, accessible research tool for dexterous manipulation that overcomes these limitations!

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Irmak Guzey

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112,023 görüntüleme • 1 yıl önce •via X (Twitter)

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RUKA is a tendon-driven, 3D-printable robotic hand. It is fully open-source, costs under $1300, and can be assembled in under 7 hours by a first-time builder. RUKA features 15 degrees of freedom and successfully performs 31 out of 33 grasps in a standard grasp taxonomy.

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To keep costs low and control both effortless and precise, RUKA leverages data-driven methods. We autonomously collect data using a MANUS glove attached to RUKA and learn a controller that maps fingertip positions to motor commands.

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This approach enables precise control and teleoperation for a wide range of tasks. RUKA can be controlled at 40 Hz and teleoperated at 25 Hz using mainstream devices like motion-capture gloves or VR headsets.

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To evaluate its capabilities in policy learning, we ran HuDOR on RUKA across two tasks. Given an open-loop trajectory from human demonstrations, a residual motor policy was learned in under 45 minutes and 40 epochs, successfully improving upon the open-loop and maximizing reward.

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To make RUKA accessible to others, we provide a calibration script that determines motor ranges after assembly. This eliminates tensioning differences across builds. With this script, controllers trained on one RUKA hand can be used on another, with 3mm difference in fingertips.

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We provide detailed assembly instructions, 3D models and a discord server for all the new time builders / users to talk and ask questions about the hand!

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Irmak Guzey1 yıl önce

It was a lot of fun being part of RUKA! This project was led by me and @anyazorin, and wouldn’t have been possible without our amazing collaborators: @BillyYYan, @AadhithyaIyer, Lisa Kondrich, @NXBhattasali, and @LerrelPinto.

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You can start building your own RUKA today! 📜Arxiv: 🔗 Website: 🏗️Assembly Instructions: 💬 Discord Server: Code and controller weights will be released next week — stay tuned!

Josh Ohara profil fotoğrafı
Josh Ohara1 yıl önce

Awesome

H4sh3 profil fotoğrafı
H4sh31 yıl önce

there will be a need to create increased torgue for canning jar. great steps to collect data

R2rule profil fotoğrafı
R2rule1 yıl önce

Why 5 fingers? People only use 3 fingers for 99.9% of their daily work. Is it worth adding extra actuators for unused fingers and increasing the cost?

Riyansh Shah profil fotoğrafı
Riyansh Shah1 yıl önce

awesome! Thank you for sharing. Can I get the link to the software documentation?

Irmak Guzey profil fotoğrafı
Irmak Guzey1 yıl önce

Hello thank you very much! :) We will share the code and the software documentation next week!

Gareth Manning profil fotoğrafı
Gareth Manning1 yıl önce

This is super cool.

wwwrobot profil fotoğrafı
wwwrobot1 yıl önce

Incredible work here!

Carlos DP 🤖🇺🇸 profil fotoğrafı
Carlos DP 🤖🇺🇸1 yıl önce

Oh wow, this is awesome!

EIDON AI profil fotoğrafı
EIDON AI1 yıl önce

Impressive! Notable that the actuators are Dynamixel vs. feetech. Also very curious on the teleop device. Do you mind sharing specs for it? We're gonna give it a try and try to teleop with our glove:

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EIDON AI1 yıl önce

Also curious what is the arm it is attached to in the saw and nutella demonstration video?

DeBarra Shaw profil fotoğrafı
DeBarra Shaw1 yıl önce

Impressive work.

Ganesh Iyer profil fotoğrafı
Ganesh Iyer1 yıl önce

This is awesome Irmak! :)

David Bar profil fotoğrafı
David Bar1 yıl önce

looks great

Francisco Cordoba profil fotoğrafı
Francisco Cordoba1 yıl önce

This is incredible!!

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What if a dexterous robot could learn a 20+ step chemistry experiment without a single on-robot training demo? Meet TwinDEX: a pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment. The twinned design shares identical kinematics, contact surfaces, visual appearance, and sensors across collection and deployment — keeping observations and actions aligned end to end. Trained from scratch on only a few hundred wearable demonstrations - with zero on-robot training or intervention data - TwinDEX completed a standardized chemistry experiment involving tool switches, fine force control, and bimanual coordination. Robot-free data showed comparable learning efficiency on the multi-task evaluation, TwinDEX delivered 5.3 times effective throughput than on-robot teleoperation. TwinDEX demonstrates that high-quality robot-free data can fully substitute for on-robot teleoperation data on challenging dexterous tasks — removing the dependency on real-robot hardware that has been the central bottleneck to scaling dexterous manipulation data. This was the proof-it phase. Now comes scale: what emerges at tens of thousands, or millions, of episodes? Watch the demo and read the technical blog: #TwinDEX #Robotics #EmbodiedAI #DexterousManipulation

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📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap data can be transferred to Inspire, Shadow, Xhand, Allegro, and Mano. With ManipTrans, we can scale up dex manip data greatly with minimal effort. For more details, please check our -webpage: -code: -huggingface:
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📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap data can be transferred to Inspire, Shadow, Xhand, Allegro, and Mano. With ManipTrans, we can scale up dex manip data greatly with minimal effort. For more details, please check our -webpage: -code: -huggingface:

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Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing discuss: We present a novel approach to synthesize dexterous motions for physically simulated hands in tasks that require coordination between the control of two hands with high temporal precision. Instead of directly learning a joint policy to control two hands, our approach performs bimanual control through cooperative learning where each hand is treated as an individual agent. The individual policies for each hand are first trained separately, and then synchronized through latent space manipulation in a centralized environment to serve as a joint policy for two-hand control. By doing so, we avoid directly performing policy learning in the joint state-action space of two hands with higher dimensions, greatly improving the overall training efficiency. We demonstrate the effectiveness of our proposed approach in the challenging guitar-playing task. The virtual guitarist trained by our approach can synthesize motions from unstructured reference data of general guitar-playing practice motions, and accurately play diverse rhythms with complex chord pressing and string picking patterns based on the input guitar tabs that do not exist in the references. Along with this paper, we provide the motion capture data that we collected as the reference for policy training.
6:21

Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing discuss: We present a novel approach to synthesize dexterous motions for physically simulated hands in tasks that require coordination between the control of two hands with high temporal precision. Instead of directly learning a joint policy to control two hands, our approach performs bimanual control through cooperative learning where each hand is treated as an individual agent. The individual policies for each hand are first trained separately, and then synchronized through latent space manipulation in a centralized environment to serve as a joint policy for two-hand control. By doing so, we avoid directly performing policy learning in the joint state-action space of two hands with higher dimensions, greatly improving the overall training efficiency. We demonstrate the effectiveness of our proposed approach in the challenging guitar-playing task. The virtual guitarist trained by our approach can synthesize motions from unstructured reference data of general guitar-playing practice motions, and accurately play diverse rhythms with complex chord pressing and string picking patterns based on the input guitar tabs that do not exist in the references. Along with this paper, we provide the motion capture data that we collected as the reference for policy training.

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Engineers at 1X have developed a 25-degree-of-freedom humanoid robot hand for advanced dexterous manipulation. It uses force sensors in every joint and fingertips to detect pressure, slipping, and automatically adjust its grip. Its lightweight, waterproof design allows the NEO humanoid robot to safely wash its hands after cooking or other messy tasks.

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Thrilled to share one of my favorite works this year: DexNDM! We bridge the Sim2Real gap for dexterous in-hand rotation, achieving a true "0-to-1" advancement. The key? DexNDM learns from biased, real-world data without needing any successful demonstrations. Now a general-purpose dexterous hand can stably rotate large books, long rods, & complex objects around any axis, from any wrist pose. This powerful primitive enables complex, long-horizon tasks like teleoperated screwing and furniture assembly. 📄 Paper: 🌐 Project Page:

Li Yi

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Robotics just hit a dexterity milestone 🤯! Sharpa Robotics demonstrated autonomous dual-hand apple peeling using its new MoDE-VLA system. Using human-like dexterous hands with tactile sensing, the robot can feel contact and adjust its grip while rotating and peeling the apple. While the system achieved 30% success, the 73% peel completion rate shows robots are starting to master the fine, contact-rich manipulation needed for real world tasks.
0:30

Robotics just hit a dexterity milestone 🤯! Sharpa Robotics demonstrated autonomous dual-hand apple peeling using its new MoDE-VLA system. Using human-like dexterous hands with tactile sensing, the robot can feel contact and adjust its grip while rotating and peeling the apple. While the system achieved 30% success, the 73% peel completion rate shows robots are starting to master the fine, contact-rich manipulation needed for real world tasks.

SciTech Era

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Dexterous hands are one of the biggest challenges in robotics, with even Elon Musk noting their development accounts for half of a humanoid robot's total work. A new approach from the Y-Hand M1 bionic hand may be a significant step forward. Based on an international first-of-its-kind "bionic tension-compression theory," the M1 replaces traditional rigid hinges with a 6-DoF joint system and uses self-developed magnetic artificial muscles. This design allows it to perform complex, human-like tasks, including: ► Mastering all 33 Feix dexterous grasp categories. ► Precisely twirling playing cards and threading a needle with subtle human-like adjustments. ► Smoothly twisting bottle caps and turning book pages. The team behind the M1 is already working on the next-gen Y-Hand M3, aiming for around 70 degrees of freedom, believing that robotic hands may soon reach or even exceed human-level dexterity.
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Dexterous hands are one of the biggest challenges in robotics, with even Elon Musk noting their development accounts for half of a humanoid robot's total work. A new approach from the Y-Hand M1 bionic hand may be a significant step forward. Based on an international first-of-its-kind "bionic tension-compression theory," the M1 replaces traditional rigid hinges with a 6-DoF joint system and uses self-developed magnetic artificial muscles. This design allows it to perform complex, human-like tasks, including: ► Mastering all 33 Feix dexterous grasp categories. ► Precisely twirling playing cards and threading a needle with subtle human-like adjustments. ► Smoothly twisting bottle caps and turning book pages. The team behind the M1 is already working on the next-gen Y-Hand M3, aiming for around 70 degrees of freedom, believing that robotic hands may soon reach or even exceed human-level dexterity.

RoboHub🤖

64,560 görüntüleme • 1 yıl önce

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, Zekai Wang, Boning Shao Haoran Lu, trevordarrell, Ismini Lourentzou, Wei ZHAN
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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, Zekai Wang, Boning Shao Haoran Lu, trevordarrell, Ismini Lourentzou, Wei ZHAN

Berkeley AI Research

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