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OpenJaws is a robot-learning data-collection paper. It replaces monocular visual-inertial odometry with a low-cost stereo camera to get metric end-effector trajectories for handheld demonstrations, and it releases hardware, software, and a localization benchmark under a permissive license. For AI readers, the main point is that better action labels from cheap, open hardware could make imitation-learning datasets for manipulation policies more accurate and easier to collect, though the evidence so far is a single benchmark and a limited real-robot demo.
Sursă care acoperăarXiv

OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.36598v1 Announce Type: new Abstract: Handheld data-collection interfaces such as the Universal Manipulation Interface (UMI) allow manipulation demonstrations to be gathered in the wild without a robot in the loop, but the quality of the resulting action labels is bounded by the accuracy of vision-based localization. Existing systems recover end-effector pose from monocular visual-inertial odometry, which is subject to scale ambiguity and drift, or delegate tracking to closed-source modules and external infrastructure that reintroduce cost and setup burden. We present OpenJaws, a fully open hardware and software framework for sensorized UMI that improves localization and simplifies deployment across robot platforms. OpenJaws pairs a modular, 3D-printed gripper exposing a standardized ISO adapter with a low-cost stereo camera to recover metric camera trajectories. We further introduce UMI-Loc-Bench-v1, a benchmark quantifying real-world localization accuracy for handheld data-collection devices, and show that stereo localization substantially outperforms monocular alternatives. Finally, we demonstrate that policies trained on data collected with OpenJaws can be deployed on a real robot across multiple tasks. Hardware, software, and data are released under a commercially permissive license. Code, data, and videos are available at https://open-jaws.github.io/

Abstract:Handheld data-collection interfaces such as the Universal Manipulation Interface (UMI) allow manipulation demonstrations to be gathered in the wild without a robot in the loop, but the quality of the resulting action labels is bounded by the accuracy of vision-based localization. Existing systems recover end-effector pose from monocular visual-inertial odometry, which is subject to scale ambiguity and drift, or delegate tracking to closed-source modules and external infrastructure that reintroduce cost and setup burden. We present OpenJaws, a fully open hardware and software framework for sensorized UMI that improves localization and simplifies deployment across robot platforms. OpenJaws pairs a modular, 3D-printed gripper exposing a standardized ISO adapter with a low-cost stereo camera to recover metric camera trajectories. We further introduce UMI-Loc-Bench-v1, a benchmark quantifying real-world localization accuracy for handheld data-collection devices, and show that stereo localization substantially outperforms monocular alternatives. Finally, we demonstrate that policies trained on data collected with OpenJaws can be deployed on a real robot across multiple tasks. Hardware, software, and data are released under a commercially permissive license. Code, data, and videos are available at this https URL

Robotics (cs.RO)

arXiv:2610.36598 [cs.RO]

(or arXiv:2610.36598v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.36598 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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