用机器人精准复现人类动作，提升AR评估的可靠性
A High-Fidelity Robotic Manipulator Teleoperation Framework for Human-Centered Augmented Reality Evaluation
- 通过定制视觉与惯性融合算法捕捉手腕运动，结合手机应用实现自然6自由度控制
- 采用主动安全的二次规划控制器，确保机器人运动平稳无抖动
- 开源平台与132条轨迹数据集，支持可重复的AR系统评测
验证增强现实（AR）追踪与交互模型需要精确可重复的真值运动数据。然而，由于生物力学差异，人类用户难以保持动作一致性。若机器人能模仿人类运动，则可作为理想的动作代理。本文设计并实现了ARBot，一个实时遥操作平台，能够有效捕捉自然人类动作，并通过机械臂精确重放。ARBot包含两种采集模式：基于自定义计算机视觉与惯性测量单元（IMU）管道的稳定腕部运动捕捉，以及通过移动端应用实现的自然6自由度控制。我们设计了主动安全的二次规划（QP）控制器，确保机械臂执行平滑、无抖动，从而作为高保真记录与重放物理代理。我们开源了ARBot，并发布了一个包含132条人类与合成轨迹的基准数据集，以支持可控且可扩展的AR评估。
原文摘要 · Abstract (English)
Validating Augmented Reality (AR) tracking and interaction models requires precise, repeatable ground-truth motion. However, human users cannot reliably perform consistent motion due to biomechanical variability. Robotic manipulators are promising to act as human motion proxies if they can mimic human movements. In this work, we design and implement ARBot, a real-time teleoperation platform that can effectively capture natural human motion and accurately replay the movements via robotic manipulators. ARBot includes two capture models: stable wrist motion capture via a custom CV and IMU pipeline, and natural 6-DOF control via a mobile application. We design a proactively-safe QP controller to ensure smooth, jitter-free execution of the robotic manipulator, enabling it to function as a high-fidelity record and replay physical proxy. We open-source ARBot and release a benchmark dataset of 132 human and synthetic trajectories captured using ARBot to support controllable and scalable AR evaluation.
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