外观
论文arXiv 2610.0985721 天前
面向 UMI 式机器人操作教学的精确末端执行器定位
原文链接：Towards Accurate End-Effector Localization for UMI-Style Robotic Manipulation Teaching（arXiv 2610.09857 · 发布日期：2026-10-07）
Towards Accurate End-Effector Localization for UMI-Style Robotic Manipulation Teaching
针对近距操作遮挡下末端定位，提出MILD数据集与AprilVINS，并在真实记录和MILD-Sim回放中验证。
核心要点
- 现有SLAM基准偏重导航运动，操作数据集多关注策略学习，缺少定位评估。
- MILD包含真实与仿真序列；AprilVINS融合鱼眼VIO与序列局部AprilTag几何。
- 真实记录基准显示，各系统在TCP相对轨迹误差和时间覆盖上差异显著。
- AprilVINS(full)达毫米级SE(3)对齐TCP相对误差；代码等计划接收后发布。
验证范围与局限：验证范围为MILD真实子集86条序列、15个重复桌面任务及MILD-Sim仿真回放。
英文摘要 (Abstract)
Robot demonstration learning requires accurate and temporally complete end-effector localization during close-range manipulation and camera occlusion. Existing SLAM benchmarks emphasize navigation motions, whereas manipulation datasets prioritize policy learning over localization evaluation. We introduce MILD, a Manipulation-Interface Localization Dataset with real-world 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 recordings reveals large differences in both TCP-relative trajectory error and temporal coverage, even under the same nominal task. To support marker-augmented teaching workspaces without a pre-surveyed fiducial map, we present AprilVINS, which combines fisheye visual-inertial estimation with sequence-local AprilTag geometry and separates prior admission from guarded export of the jointly optimized state. On Insta360 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 task-specific tolerance references for interpreting those error magnitudes. Together, MILD and AprilVINS provide a diagnostic benchmarking framework for UMI-style demonstration collection. Code, datasets, and evaluation manifests will be released upon acceptance.
作者：Junjie Zhang, Deteng Zhang, Zhisong Xu, Bo Sun, Lun Li, Yihong Tian, Jie Yin
来源
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