Towards Accurate End-Effector Localization for UMI-Style Robotic Manipulation Teaching
Towards Accurate End-Effector Localization for UMI-Style Robotic Manipulation Teaching
This research introduces MILD, a Manipulation-Interface Localization Dataset, and AprilVINS, a method for accurate end-effector localization in robotic teaching. The dataset includes real-world and simulation sequences, and AprilVINS combines visual-inert, AprilTag geometry, and state optimization to achieve millimeter-level accuracy in TCP-relative localization.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Robot trajectoriesOpen source S3
Reported numbers
sensor sequences
86
tasks
15
- code: not_reported
- dataset: not_reported
- weights: not_reported
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
d and simulation sequences. The real-world subset provides 86 sensor sequences from Insta360 X5 and Insight9 across 15 repeated tabletop tasks, calibration assets, and a per-execution robot end-effector reference trajectory. The simulation subset, MILD-Sim, extends task coverage in Isaac Sim for controlled manipulation-replay studies. Benchmarking visual-inertial and fiducial-aided systems on instrumented real-world
Open source S3
AprilTag4 recordings, AprilVINS(full) under a unified protocol with sequence-specific profiles reaches millimeter-level SE(3)-aligned TCP-relative APE RMSE with high time completion and lower reported error than the tested routes under their respective protocols, whereas fisheye VIO without tag factors remains at centimeter scale. Ablations separate accuracy from exportability, and a MILD-Sim replay study provides t
Open source S5
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Source：arXiv Robotics — research abstracts · arxiv.org