Ubiquitous robot control and human-robot collaboration using smart devices poses a challenging problem primarily due to strict accuracy requirements and sparse information. This paper presents a novel approach that incorporates a probabilistic differentiable filter, specifically the Differentiable Ensemble Kalman Filter (DEnKF), to facilitate robot control solely using Inertial Measurement Units (IMUs) from a smartwatch and a smartphone. The implemented system is cost-effective and achieves accurate estimation of the human pose state. Experiment results from human-robot handover tasks underscore that smart devices allow versatile and ubiquitous robot control. The code for this paper is available at https://github.com/ir-lab/DEnKF and https://github.com/wearable-motion-capture.

翻译：利用智能设备实现普适机器人控制与人机协作面临着严苛精度要求与稀疏传感信息导致的严峻挑战。本文提出了一种创新方法，通过集成可概率可微分滤波器——具体为可微分集成卡尔曼滤波器（DEnKF），仅利用智能手表与智能手机中的惯性测量单元（IMU）即可实现机器人控制。所构建系统成本低廉，并能够准确估计人体姿态状态。针对人机交接任务的实验结果表明，智能设备可实现多功能且普适的机器人控制。本文代码详见https://github.com/ir-lab/DEnKF与https://github.com/wearable-motion-capture。