融合物理定律与数据学习，实现软体机器人的高精度能量守恒建模。
Learning-Based Modeling of Soft Robots via Cosserat Rod Theory
- 结合柯西杆理论与哈密顿物理，构建能量结构保持的模型。
- 仿真验证可精准捕捉杆状软体机器人的动态行为并保持能量守恒。
- 适合需要物理可解释性与稳定性控制的研究者使用。
由于连续体结构和典型的非线性动力学，软体机器人动力学建模极具挑战。基于第一性原理的方法通常耗时且表达能力有限，而数据驱动模型则缺乏可解释性和物理一致性。本文提出一种基于端口-哈密顿高斯过程回归的框架，用于学习和模拟平面杆状软体机器人的动力学。该模型将柯西杆理论与哈密顿物理相结合，通过数据驱动推断，在保持系统能量结构的同时准确学习杆状结构的动力学特性。数值仿真表明，该方法能够实现对杆状软体机器人精确且能量一致的表征，展现出一种稳健且可解释的复杂连续介质力学建模路径。
原文摘要 · Abstract (English)
Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order principles is typically time-demanding, and their expressiveness is limited, whereas data-driven models lack interpretability and physical consistency. This work aims to overcome these challenges by introducing a port-Hamiltonian Gaussian Process Regression framework for learning and simulating the dynamics of planar, rod-like soft robots. In detail, the proposed model integrates Cosserat rod theory and Hamiltonian physics with data-driven inference to preserve the system's energy structure while accurately learning the rod dynamics. Numerical simulations show that we can achieve accurate and energy-consistent representations of a rod-like soft robot, showing the potential for a robust and interpretable pathway for modeling complex continuum mechanics.
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