用物理约束神经网络，让软体机器人实时控制更快更准。
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
- 用物理信息神经网络，仅需少量真实数据训练
- 预测速度比传统模型快467倍，精度略有下降
- 适合需要快速高精度控制的软体机器人场景
软体机器人在灵活性与安全性要求高的场景中具有巨大潜力。实时估计与控制依赖快速准确的模型，但基于物理原理（FP）的模型计算慢，黑箱学习模型泛化能力差。本文提出用于铰接式软体机器人（ASRs）的物理信息神经网络（PINNs），重点提升数据效率：仅需一个系统领域的少量真实数据（约两小时不同域数据）。相比循环神经网络，该方法具备更高泛化能力；在略降低精度的前提下，预测速度较精确的FP模型提升达467倍。该模型支持非线性模型预测控制（MPC），在六次动态实验中实现47 Hz运行下的精准位置跟踪。
原文摘要 · Abstract (English)
Soft robots can revolutionize several applications with high demands on dexterity and safety. When operating these systems, real-time estimation and control require fast and accurate models. However, prediction with first-principles (FP) models is slow, and learned black-box models have poor generalizability. Physics-informed machine learning offers excellent advantages here, but it is currently limited to simple, often simulated systems without considering changes after training. We propose physics-informed neural networks (PINNs) for articulated soft robots (ASRs) with a focus on data efficiency. The amount of expensive real-world training data is reduced to a minimum -- one dataset in one system domain. Two hours of data in different domains are used for a comparison against two gold-standard approaches: In contrast to a recurrent neural network, the PINN provides a high generalizability. The prediction speed of an accurate FP model is exceeded with the PINN by up to a factor of 467 at slightly reduced accuracy. This enables nonlinear model predictive control (MPC) of a pneumatic ASR. Accurate position tracking with the MPC running at 47 Hz is achieved in six dynamic experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。