# YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale arXiv: https://arxiv.org/abs/2606.10244 · HTML: https://mlsanity.com/paper/2606.10244/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 48 references that are in the map. - First posted on arXiv 8 Jun 2026. ## Builds on (most cited first, 30 of 37) - [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023) - [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021) - [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015) - [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023) - [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024) - [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016) - [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024) - [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024) - [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023) - [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023) - [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024) - [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024) - [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023) - [Open-TeleVision: Teleoperation with Immersive Active Visual Feedback](https://mlsanity.com/paper/2407.01512/) (2024) - [Data Scaling Laws in Imitation Learning for Robotic Manipulation](https://mlsanity.com/paper/2410.18647/) (2024) - [Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos](https://mlsanity.com/paper/2507.15597/) (2025) - [DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation](https://mlsanity.com/paper/2505.21864/) (2025) - [AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation](https://mlsanity.com/paper/2304.12301/) (2023) - [EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World](https://mlsanity.com/paper/2604.07607/) (2026) - [AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild](https://mlsanity.com/paper/2309.14975/) (2023) - [FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset](https://mlsanity.com/paper/2409.19499/) (2024) - [DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies](https://mlsanity.com/paper/2505.07813/) (2025) - [ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data](https://mlsanity.com/paper/2406.19464/) (2024) - [ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback](https://mlsanity.com/paper/2410.08464/) (2024) - [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024) - [ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface](https://mlsanity.com/paper/2504.06156/) (2025) - [FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation](https://mlsanity.com/paper/2506.01941/) (2025) - [ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations](https://mlsanity.com/paper/2510.01607/) (2025) ## Built on by (most cited first, 4 of 4) - [What Matters for Latent Actions in Robot Learning](https://mlsanity.com/paper/2608.19613/) (2026) - [SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation](https://mlsanity.com/paper/2608.04196/) (2026) - [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026) - [YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation](https://mlsanity.com/paper/2610.17521/) (2026) ## Abstract (arXiv) We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous manipulation. While handheld data collection systems such as Universal Manipulation Interface (UMI) enable affordable data collection, their bulky pistol-grip designs can pose ergonomic and usability challenges for fine-grained, dexterous manipulation tasks. To address this, YUBI presents a distinct design principle: yielding, finger-driven actuation that directly maps human finger movements to gripper jaw motion. Using the YUBI devices, we set up a data collection system with integrated VR-based 6 DoF tracking of the gripper, ensuring high-fidelity trajectory data acquisition. We curate a UMI-based dataset of unprecedented scale: 8,434 hours across 1.20M episodes and 119 tasks. Experiments show that YUBI offers advantages over the UMI gripper in versatility for complex bimanual tasks, dexterity, and operational efficiency. A single policy trained on the YUBI dataset transfers across multiple bimanual robots (UR, Franka, and ELEY) simply by mounting the gripper on each platform, confirming that the collected data are directly executable as policy supervision. We release the gripper hardware, data-collection software, and dataset as one integrated stack, offering the open community a reproducible path to large-scale data acquisition for advancing robotic foundation models.