Towards Accurate End-Effector Localization for UMI-Style Robotic Manipulation Teaching
Overview
New research introduces MILD dataset and AprilVINS method for millimeter-level end-effector localization in robotic teaching, combining visual-inertial and AprilTag geometry for improved accuracy in real-world and simulated environments.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
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Data origin: robot trajectories
“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”
Exact source · revision 1Source owner not reported
Reported quantity · sensor sequences: 86 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · tasks: 15 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsTowards 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.
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