Probabilistic Differentiable Filters Enable Ubiquitous Robot Control with Smartwatches
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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 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.
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