This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding process is discussed to handle observations with varying sensor placements and sampling frequencies. The efficacy of this approach is demonstrated on a tensegrity robot arm by learning end-effector dynamics from demonstrations with complex bending motions. The model is proven to be robust against missing modalities, diverse sensor placement, and varying sampling rates. Additionally, the proposed framework is shown to identify physical interactions with humans during motion. The utilization of a differentiable filter presents a novel solution to the difficulties of modeling soft robot dynamics. Our approach shows substantial improvement in accuracy compared to state-of-the-art filtering methods, with at least a 24% reduction in mean absolute error (MAE) observed. Furthermore, the predicted end-effector positions show an average MAE of 25.77mm from the ground truth, highlighting the advantage of our approach. The code is available at https://github.com/ir-lab/soft_robot_DEnKF.

翻译：本文提出了一种新颖的软体机器人动力学建模方法，该方法采用可微分滤波器架构。该方案支持端到端训练，能够自主习得系统动力学特性、噪声特征以及机器人的时间行为。文中讨论了一种创新性的时空嵌入处理机制，用于处理具有不同传感器布局与采样频率的观测数据。通过在张拉整体机器人臂上演示复杂弯曲运动的末端执行器动力学学习，验证了该方法的有效性。研究表明，该模型对模态缺失、传感器多样化布局及变采样率均展现出鲁棒性。此外，所提框架还能识别运动过程中与人类的物理交互。可微分滤波器的应用为软体机器人动力学建模难题提供了创新解决方案。相较于现有最优滤波方法，本方法平均绝对误差（MAE）显著降低至少24%。同时，末端执行器位置预测结果与真实值相比平均MAE为25.77mm，凸显了本方法的优势。相关代码已开源至https://github.com/ir-lab/soft_robot_DEnKF。