Soft actuators and their sensors have always been separate entities with two distinct roles. The omnidirectional compliance of soft robots thus means that multiple sensors have to be used to sense different modalities in the respective planes of motion. With the recent emergence of self-sensing actuators, the two roles have gradually converged to simplify sensing requirements. Self-sensing typically involves embedding a conductive sensing element into the soft actuator and provides multiple state information along the continuum. However, most of these self-sensing actuators are fabricated through manual methods, which results in inconsistent sensing performance. Soft material compliance also imply that both actuator and sensor exhibit nonlinear behaviors during actuation, making sensing more complex. In this regard, machine learning has shown promise in characterizing the nonlinear behavior of soft sensors. Beyond characterization, we show that applying machine learning to soft actuators eliminates the need to implant a sensing element to achieve self-sensing. Fabrication is done using 3D printing, thus ensuring that sensing performance is consistent across the actuators. In addition, our proposed technique is able to estimate the bending curvature of a soft continuum actuator and the external forces applied to the tip of the actuator in real time. Our methodology is generalizable and aims to provide a novel way of multimodal sensing for soft robots across a variety of applications.

中文翻译：

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一种基于学习的方法来感知软机器人

软执行器及其传感器一直是具有两个不同角色的独立实体。因此，软体机器人的全向顺应性意味着必须使用多个传感器来感测各个运动平面中的不同模态。随着最近自感应执行器的出现，这两种角色逐渐融合以简化感应要求。自感通常涉及将导电传感元件嵌入软致动器中，并提供沿连续体的多个状态信息。然而，大多数这些自感应执行器都是通过手工方法制造的，这导致了感应性能的不一致。软材料顺应性还意味着致动器和传感器在致动过程中都表现出非线性行为，使传感更加​​复杂。在这方面，机器学习在表征软传感器的非线性行为方面显示出前景。除了表征之外，我们还表明，将机器学习应用于软执行器无需植入传感元件即可实现自我传感。使用 3D 打印完成制造，从而确保执行器的传感性能一致。此外，我们提出的技术能够实时估计软连续体致动器的弯曲曲率和施加到致动器尖端的外力。我们的方法具有普遍性，旨在为各种应用的软体机器人提供一种新颖的多模态传感方式。我们表明，将机器学习应用于软执行器无需植入传感元件即可实现自我传感。使用 3D 打印完成制造，从而确保执行器的传感性能一致。此外，我们提出的技术能够实时估计软连续体致动器的弯曲曲率和施加到致动器尖端的外力。我们的方法具有普遍性，旨在为各种应用的软体机器人提供一种新颖的多模态传感方式。我们表明，将机器学习应用于软执行器无需植入传感元件即可实现自我传感。使用 3D 打印完成制造，从而确保执行器的传感性能一致。此外，我们提出的技术能够实时估计软连续体致动器的弯曲曲率和施加到致动器尖端的外力。我们的方法具有普遍性，旨在为各种应用的软体机器人提供一种新颖的多模态传感方式。我们提出的技术能够实时估计软连续体致动器的弯曲曲率和施加到致动器尖端的外力。我们的方法具有普遍性，旨在为各种应用的软体机器人提供一种新颖的多模态传感方式。我们提出的技术能够实时估计软连续体致动器的弯曲曲率和施加到致动器尖端的外力。我们的方法具有普遍性，旨在为各种应用的软体机器人提供一种新颖的多模态传感方式。