Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy that can calculate the actuator stimulation necessary to achieve a target surface. The programmability of a morphing surface can be improved by increasing the number of independent actuators, but this increases the complexity of the control system. Thus, developing compact and efficient control interfaces and control algorithms is a crucial knowledge gap for the adoption of morphing surfaces in broad applications. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of independent actuators using only 2N control inputs, which is significantly lower than control inputs required for traditional direct addressing. Our control algorithm is based on machine learning using finite element simulations as the training data. This machine learning approach allows both forward and inverse control with high precision in real time. Inverse control demonstrations show that the PARMS can dynamically morph into arbitrary pre-defined surfaces on demand. These innovations in actuator matrix control may enable future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality (AR/VR).

翻译：可重构变形表面为先进人机交互和仿生机器人学提供了新机遇。按需变形为任意曲面需要具备足够数量致动器的装置，以及能够计算实现目标曲面所需致动器激励的逆控制策略。增加独立致动器的数量可提升变形表面的可编程性，但也增加了控制系统的复杂性。因此，开发紧凑高效的控制接口与控制算法是推动变形表面广泛应用的关键知识缺口。本文描述了一种由矩阵排列离子致动器构成的被动寻址机器人变形表面（PARMS）。为降低物理控制接口的复杂性，我们引入了被动矩阵寻址技术。该矩阵寻址技术仅需2N个控制输入即可控制独立致动器，远低于传统直接寻址所需的控制输入数量。我们的控制算法基于机器学习，采用有限元仿真数据作为训练集。这种机器学习方法可同时实现高精度前馈和逆控制，且具备实时性。逆控制演示表明，PARMS能够按需动态变形为任意预设曲面。这些致动器矩阵控制技术的创新有望推动PARMS未来在可穿戴设备、触觉反馈及增强现实/虚拟现实（AR/VR）等领域的应用。