Irmak Guzey

Selected Publications

2026

1. 

   RUKA-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning

   Xinqi (Lucas) Liu,  ..., and Irmak Guzey

   Arxiv 2026 2026

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   Lack of accessible and dexterous robot hardware has been a significant bottleneck to achieving human-level dexterity in robots. Last year, we released Ruka, a fully open-sourced, tendon-driven humanoid hand with 11 degrees of freedom - 2 per finger and 3 at the thumb - buildable for under $1,300. It was one of the first fully open-sourced humanoid hands, and introduced a novel data-driven approach to finger control that captures tendon dynamics within the control system. Despite these contributions, Ruka lacked two degrees of freedom essential for closely imitating human behavior: wrist mobility and finger adduction/abduction. In this paper, we introduce Ruka-v2: a fully open-sourced, tendon-driven humanoid hand featuring a decoupled 2-DOF parallel wrist and abduction/adduction at the fingers. The parallel wrist adds smooth, independent flexion/extension and radial/ulnar deviation, enabling manipulation in confined environments such as cabinets. Abduction enables motions such as grasping thin objects, in-hand rotation, and calligraphy. We present the design of Ruka-v2 and evaluate it against Ruka through user studies on teleoperated tasks, finding a 51.3% reduction in completion time and a 21.2% increase in success rate. We further demonstrate its full range of applications for robot learning: bimanual and single-arm teleoperation across 13 dexterous tasks, and autonomous policy learning on 3 tasks. All 3D print files, assembly instructions, controller software, and videos are open-sourced.

2025

1. 

   AINA | Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations

   Irmak Guzey, Haozhi Qi, Julen Urain, and 10 more authors

   ICRA 2026 2025

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   Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark significant progress toward generalizable robot manipulation in human environments, as it would reduce the reliance on labor-intensive robot interaction data collection. Despite substantial efforts, progress toward this goal has been bottle-necked by the embodiment gap between humans and robots, as well as by difficulties in extracting relevant contextual and motion cues that enable learning of autonomous policies from in-the-wild human videos. We claim that with simple yet sufficiently powerful hardware for obtaining human data and our proposed framework AINA, we are now one significant step closer to achieving this dream. AINA enables learning multi-fingered policies from data collected by anyone, anywhere, and in any environment using Aria Gen 2 glasses. These glasses are lightweight and portable, feature a high-resolution RGB camera, provide accurate on-board 3D head and hand poses, and offer a wide stereo view that can be leveraged for depth estimation of the scene. This setup enables the learning of 3D point-based policies for multi-fingered hands that are robust to background changes and can be deployed directly without requiring any robot data (including online corrections, reinforcement learning, or simulation).

2. 

   RUKA: Rethinking the Design of Humanoid Hands with Learning

   Anya Zorin*, Irmak Guzey*, Billy Yan, and 4 more authors

   RSS 2025 2025

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   This work presents RUKA, a tendon-driven humanoid hand that is compact, affordable, and capable. Made from 3D-printed parts and off-the-shelf components, RUKA has 5 fingers with 15 underactuated degrees of freedom enabling diverse human-like grasps. Its tendon-driven actuation allows powerful grasping in a compact, human-sized form factor. To address control challenges, we learn joint-to-actuator and fingertip-to- actuator models from motion-capture data collected by the MANUS glove, leveraging the hand’s morphological accuracy. Extensive evaluations demonstrate RUKA’s superior reachability, durability, and strength compared to other robotic hands. Tele- operation tasks further showcase RUKA’s dexterous movements. The open-source design and assembly instructions of RUKA, code, and data are available on our website.

2024

1. 

   HuDOR: Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards

   Irmak Guzey, Yinlong Dai, Georgy Savva, and 2 more authors

   ICRA 2025 2024

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   Training robots directly from human videos is an emerging area in robotics and computer vision. While there has been notable progress with two-fingered grippers, learning autonomous tasks without teleoperation remains a difficult problem for multi-fingered robot hands. A key reason for this difficulty is that a policy trained on human hands may not directly transfer to a robot hand with a different morphology. In this work, we present HUDOR, a technique that enables online fine-tuning of the policy by constructing a reward function from the human video. Importantly, this reward function is built using object-oriented rewards derived from off-the-shelf point trackers, which allows for meaningful learning signals even when the robot hand is in the visual observation, while the human hand is used to construct the reward. Given a single video of human solving a task, such as gently opening a music box, HUDOR allows our four-fingered Allegro hand to learn this task with just an hour of online interaction. Our experiments across four tasks, show that HUDOR outperforms alternatives with an average of 4× improvement.

2. 

   OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation

   Aadhithya Iyer, Zhuoran Peng, Yinlong Dai, and 4 more authors

   CORL 2024 2024

   Abs Website

   Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in multi-fingered dexterity, bimanual manipulation, and applica- tions ranging from logistics to home robotics. However, existing data collection platforms are often proprietary, costly, or tailored to specific robotic morphologies. We present OPEN TEACH, a new teleoperation system leveraging VR headsets to immerse users in mixed reality for intuitive robot control. Built on the affordable Meta Quest 3, which costs $500, OPEN TEACH enables real- time control of various robots, including multi-fingered hands, bimanual arms, and mobile manipulators, through an easy-to- use app. Using natural hand gestures and movements, users can manipulate robots at up to 90Hz with smooth visual feedback and interface widgets offering closeup environment views. We demonstrate the versatility of OPEN TEACH across 38 tasks on different robots. A comprehensive user study indicates significant improvement in teleoperation capability over the AnyTeleop framework. Further experiments exhibit that the collected data is compatible with policy learning on 10 dexterous and contact- rich manipulation tasks. Currently supporting Franka, xArm, Jaco, Allegro, and Hello Stretch platforms, OPEN TEACH is fully open-sourced to promote broader adoption.

2023

1. 

   See to Touch: Learning Tactile Dexterity through Visual Incentives

   Irmak Guzey, Yinlong Dai, Ben Evans, and 2 more authors

   ICRA 2024 2023

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   Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on tactile sensing fails to provide adequate cues for reasoning about objects’ spatial configurations, limiting the ability to correct errors and adapt to changing situations. In this paper, we present Tactile Adaptation from Visual Incentives (TAVI), a new framework that enhances tactile-based dexterity by optimizing dexterous policies using vision-based rewards. First, we use a contrastive-based objective to learn visual representations. Next, we construct a reward function using these visual representations through optimal-transport based matching on one human demonstration. Finally, we use online reinforcement learning on our robot to optimize tactile-based policies that maximize the visual reward. On six challenging tasks, such as peg pick-and-place, unstacking bowls, and flipping slender objects, TAVI achieves a success rate of 73% using our four-fingered Allegro robot hand. The increase in performance is 108% higher than policies using tactile and vision-based rewards and 135% higher than policies without tactile observational input.

2. 

   Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play

   Irmak Guzey, Ben Evans, Soumith Chintala, and 1 more author

   CoRL 2023 2023

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   Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either operate on visual observations or state estimates derived from vision. However, such methods perform poorly on fine-grained manipulation tasks that require reasoning about contact forces or about objects occluded by the hand itself. In this work, we present T-Dex, a new approach for tactile-based dexterity, that operates in two phases. In the first phase, we collect 2.5 hours of play data, which is used to train self-supervised tactile encoders. This is necessary to bring high-dimensional tactile readings to a lower-dimensional embedding. In the second phase, given a handful of demonstrations for a dexterous task, we learn non-parametric policies that combine the tactile observations with visual ones. Across five challenging dexterous tasks, we show that our tactile-based dexterity models outperform purely vision and torque-based models by an average of 1.7X. Finally, we provide a detailed analysis on factors critical to T-Dex including the importance of play data, architectures, and representation learning.

2022

1. 

   Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

   Sridhar Pandian Arunachalam, Irmak Guzey, Soumith Chintala, and 1 more author

   ICRA 2023 2022

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   A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional, contact-rich behaviors often require esoteric teleoperation tools. In this work, we present HOLO-DEX, a framework for dexterous manipulation that places a teacher in an immersive mixed reality through commodity VR headsets. The high-fidelity hand pose estimator onboard the headset is used to teleoperate the robot and collect demonstrations for a variety of generalpurpose dexterous tasks. Given these demonstrations, we use powerful feature learning combined with non-parametric imitation to train dexterous skills. Our experiments on six common dexterous tasks, including in-hand rotation, spinning, and bottle opening, indicate that HOLO-DEX can both collect high-quality demonstration data and train skills in a matter of hours. Finally, we find that our trained skills can exhibit generalization on objects not seen in training.

2020

1. 

   Human Motion Prediction With Graph Neural Networks

   Irmak Guzey, Ahmet E. Tekden, Evren Samur, and 1 more author

   2020

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   In this work, we propose to use graph neural networks, propagation networks in particular, to investigate the problem of modelling full-body motion. The body parts and the relations between them are encoded as the nodes of a graph and edges between these nodes. How the nodes are related to each other is learned, and how the effects of multiple nodes on each node should be accumulated is computed in graph structure.