Soft robots have many advantages over rigid robots thanks to their compliant and passive nature. However, it is generally challenging to model the dynamics of soft robots due to their high spatial dimensionality, making it difficult to use model-based methods to accurately control soft robots. It often requires direct numerical simulation of partial differential equations to simulate soft robots. This not only requires an accurate numerical model, but also makes soft robot modeling slow and expensive. Deep learning algorithms have shown promises in data-driven modeling of soft robots. However, these algorithms usually require a large amount of data, which are difficult to obtain in either simulation or real-world experiments of soft robots. In this work, we propose KNODE-Cosserat, a framework that combines first-principle physics models and neural ordinary differential equations. We leverage the best from both worlds -- the generalization ability of physics-based models and the fast speed of deep learning methods. We validate our framework in both simulation and real-world experiments. In both cases, we show that the robot model significantly improves over the baseline models under different metrics.

翻译：软体机器人凭借其柔顺性与被动特性，相比刚性机器人具有诸多优势。然而，由于软体机器人具有高空间维度特性，对其动力学进行建模通常具有挑战性，这使得基于模型的方法难以精确控制软体机器人。模拟软体机器人往往需要对偏微分方程进行直接数值求解，这不仅需要精确的数值模型，还导致软体机器人建模过程缓慢且计算成本高昂。深度学习算法在软体机器人的数据驱动建模中展现出潜力，但这类算法通常需要大量数据，而在软体机器人的仿真或实际实验中获取这些数据均较为困难。本研究提出KNODE-Cosserat框架，该框架将第一性原理物理模型与神经常微分方程相结合。我们融合了两类方法的优势——物理模型具有的泛化能力与深度学习方法的快速计算特性。我们在仿真实验和实际实验中均验证了该框架的有效性。两种实验结果表明，在不同评价指标下，该机器人模型均显著优于基线模型。