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) observations from a smartwatch and a smartphone. The implemented system achieves accurate estimation of human pose state with a reduction of 30.2% compared to the baseline using the Mean Per Joint Vertex Error (MPJVE). Our results foster smartwatches and smartphones as a cost-effective alternative human-pose state estimation. Furthermore, experiment results from human-robot handover tasks underscore that smart devices allow for low-cost, versatile and ubiquitous robot control.

翻译：普适机器人控制及利用智能设备的人机协作面临严格精度要求和稀疏信息带来的挑战。本文提出一种融合概率可微滤波器的新方法，具体采用可微集成卡尔曼滤波器（DEnKF），仅基于智能手表与智能手机的惯性测量单元（IMU）观测数据实现机器人控制。实际系统以平均每关节顶点误差（MPJVE）为指标，相比基线方法将人体姿态估计精度提升30.2%。研究结果验证了智能手表与智能手机可作为高性价比的人体姿态估计替代方案。此外，人机交接任务实验表明，智能设备能够实现低成本、灵活且普适的机器人控制。