Soft continuum bodies have demonstrated their effectiveness in generating flexible and adaptive functionalities by capitalizing on the rich deformability of soft material. Compared with a rigid-body robot, it is in general difficult to model and emulate the morphology dynamics of a soft continuum body. In addition, a soft continuum body potentially has an infinite degree of freedom, requiring considerable labor to manually annotate its dynamics from external sensory data such as video. In this study, we propose a novel noninvasive framework for automatically extracting the skeletal dynamics from video of a soft continuum body and show the applications and effectiveness of our framework. First, we demonstrate that our framework can extract skeletal dynamics from animal videos, which can be effectively utilized for the analysis of soft continuum body including animal motion. Next, we focus on a soft continuum arm, a commonly used platform in soft robotics, and evaluate the potential information-processing capability. Normally, to control such a high-dimensional system, it is necessary to introduce many sensors to completely capture the motion dynamics, causing the deterioration of the material's softness. We illustrate that the evaluation of the memory capacity and sensory reconstruction error enables us to verify the minimum number of sensors sufficient for fully grasping the state dynamics, which is highly useful in designing a sensor arrangement for a soft robot. Also, we release the software developed in this study as open source for biology and soft robotics communities, which contributes to automating the annotation process required for the motion analysis of soft continuum bodies.

中文翻译：

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从视频中骨架化软连续体的动力学

软连续体通过利用软材料的丰富可变形性，证明了它们在产生灵活和自适应功能方面的有效性。与刚体机器人相比，一般难以对软连续体的形态动力学进行建模和仿真。此外，一个柔软的连续体可能具有无限的自由度，需要大量的人力从外部感官数据（如视频）中手动注释其动态。在这项研究中，我们提出了一种新的非侵入性框架，用于从软连续体的视频中自动提取骨骼动力学，并展示了我们框架的应用和有效性。首先，我们证明我们的框架可以从动物视频中提取骨骼动力学，可以有效地用于分析包括动物运动在内的软连续体。接下来，我们专注于软连续臂，软机器人中常用的平台，并评估潜在的信息处理能力。通常，要控制这样一个高维系统，需要引入许多传感器来完整捕捉运动动态，从而导致材料柔软度的恶化。我们说明，记忆容量和感觉重建误差的评估使我们能够验证足以完全掌握状态动态的最小传感器数量，这对于设计软机器人的传感器布置非常有用。此外，我们将本研究中开发的软件作为开源软件发布给生物学和软机器人社区，