Accurately modeling soft robots in simulation is computationally expensive and commonly falls short of representing the real world. This well-known discrepancy, known as the sim-to-real gap, can have several causes, such as coarsely approximated geometry and material models, manufacturing defects, viscoelasticity and plasticity, and hysteresis effects. Residual physics networks learn from real-world data to augment a discrepant model and bring it closer to reality. Here, we present a residual physics method for modeling soft robots with large degrees of freedom. We train neural networks to learn a residual term -- the modeling error between simulated and physical systems. Concretely, the residual term is a force applied on the whole simulated mesh, while real position data is collected with only sparse motion markers. The physical prior of the analytical simulation provides a starting point for the residual network, and the combined model is more informed than if physics were learned tabula rasa. We demonstrate our method on 1) a silicone elastomeric beam and 2) a soft pneumatic arm with hard-to-model, anisotropic fiber reinforcements. Our method outperforms traditional system identification up to 60%. We show that residual physics need not be limited to low degrees of freedom but can effectively bridge the sim-to-real gap for high dimensional systems.

翻译：在仿真环境中精确建模软体机器人计算成本高昂，且通常难以准确反映真实世界。这种被称为“仿真到现实鸿沟”的显著差异可能源于多种因素，例如粗糙的几何与材料模型近似、制造缺陷、粘弹性与塑性行为以及迟滞效应。残差物理网络通过从真实世界数据中学习来增强存在偏差的模型，使其更贴近现实。本文提出了一种适用于高自由度软体机器人的残差物理建模方法。我们训练神经网络学习残差项——即仿真系统与物理系统之间的建模误差。具体而言，该残差项作用于整个仿真网格的力，而真实位置数据仅通过稀疏运动标记采集。解析仿真的物理先验为残差网络提供了初始学习基点，相较于从零开始学习物理规律，联合模型能够获得更全面的信息。我们分别在以下两个案例中验证了该方法的有效性：1）硅胶弹性梁；2）具有难以建模的各向异性纤维增强结构的软体气动臂。实验表明，该方法相比传统系统辨识方法性能提升高达60%。研究证明，残差物理方法不仅适用于低自由度系统，更能有效弥合高维系统的仿真到现实鸿沟。