用数据驱动方法提升软体机器人运动建模精度并量化预测不确定性
Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
- 采用集成学习方法构建软体机器人非线性运动模型
- 通过分片共形预测实现位置预测不确定性的理论保障
- 适合需要高可靠性建模的软体机器人控制与校准场景
精确的运动学建模对软体机器人的标定和控制器设计至关重要，但其高度非线性和复杂行为使其建模极具挑战。针对此问题，已有多种数据驱动的机器学习方法被提出用于建模非线性动力学，然而这些模型存在预测不确定性，影响建模精度，且软体机器人运动学建模中的不确定性量化研究仍不充分。本文利用有限的仿真与真实数据，首先比较了多种常用的线性和非线性机器学习模型在软体机器人运动建模中的表现，结果表明非线性集成方法具有最强的泛化能力。随后，我们提出一种基于分片共形预测的共形运动学建模框架，可对预测位置进行不确定性量化，确保无需分布假设的预测区间，并具备理论保证。
原文摘要 · Abstract (English)
Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To tackle the issue, numerous data-driven machine learning approaches have been proposed for modeling nonlinear dynamics. However, these models suffer from prediction uncertainty that can negatively affect modeling accuracy, and uncertainty quantification for kinematic modeling in soft robots is underexplored. In this work, using limited simulation and real-world data, we first investigate multiple linear and nonlinear machine learning models commonly used for kinematic modeling of soft robots. The results reveal that nonlinear ensemble methods exhibit the most robust generalization performance. We then develop a conformal kinematic modeling framework for soft robots by utilizing split conformal prediction to quantify predictive position uncertainty, ensuring distribution-free prediction intervals with a theoretical guarantee.
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