Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoelastic material properties. Conventional control design methods often rely on either complex system modeling or time-intensive manual tuning, both of which require significant amounts of human expertise and thus limit their practicality. In recent works, the data-driven method, Automatic Neural ODE Control (ANODEC) has been successfully used to -- fully automatically and utilizing only input-output data -- design controllers for various nonlinear systems in silico, and without requiring prior model knowledge or extensive manual tuning. In this work, we successfully apply ANODEC to automatically learn to perform agile, non-repetitive reference tracking motion tasks in a real-world SR and within a finite time horizon. To the best of the authors' knowledge, ANODEC achieves, for the first time, performant control of a SR with hysteresis effects from only 30 seconds of input-output data and without any prior model knowledge. We show that for multiple, qualitatively different and even out-of-training-distribution reference signals, a single feedback controller designed by ANODEC outperforms a manually tuned PID baseline consistently. Overall, this contribution not only further strengthens the validity of ANODEC, but it marks an important step towards more practical, easy-to-use SRs that can automatically learn to perform agile motions from minimal experimental interaction time.

翻译：许多应用领域，如医疗和制造业，都能从气动软体机器人中极大获益。然而，软体机器人的精确控制至今仍是一个重大挑战，这主要源于其非线性动力学特性和粘弹性材料属性。传统的控制设计方法通常依赖于复杂的系统建模或耗时长的手动调参，这两种方式都需要大量的人类专业知识，从而限制了其实用性。在近期研究中，数据驱动方法——自动神经常微分方程控制——已成功应用于多种非线性系统的控制器设计，该过程完全自动化且仅利用输入输出数据，无需先验模型知识或大量手动调参。本工作中，我们成功应用自动神经常微分方程控制，使真实世界的软体机器人在有限时间范围内自动学习执行敏捷、非重复的参考轨迹跟踪任务。据作者所知，这是首次仅利用30秒的输入输出数据且无需任何先验模型知识，就实现了对具有迟滞效应的软体机器人的高性能控制。我们证明，对于多个性质不同甚至超出训练分布范围的参考信号，由自动神经常微分方程控制设计的单一反馈控制器始终优于手动调参的PID基线。总体而言，这项贡献不仅进一步验证了自动神经常微分方程控制的有效性，更标志着向更实用、易用的软体机器人迈出了重要一步——这类机器人能够通过最少的实验交互时间自动学习执行敏捷运动。