用物理模型提升机器人预测在异常情况下的可靠性
Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models
- 通过共形预测识别异常数据，自动切换到物理一致性模型
- 基于高斯过程学习能量函数，实现动态建模与不确定性量化
- 适合需要高可靠性的自动驾驶、机器人规划等场景
预测周围物体和障碍物的运动轨迹是许多机器人应用的关键。当系统动力学未知时，常用数据驱动方法进行状态预测。然而，面对训练数据之外的分布外观测，传统数据驱动预测器的性能、可靠性及不确定性估计会显著下降。本文提出一种即插即用的物理信息机器学习（PnP-PIML）框架，利用共形预测识别异常动力学，在此情况下从标准预测器切换至物理一致的分布式端口-哈密顿系统（dPHS）。我们采用高斯过程建模dPHS的能量函数，不仅可学习系统动态，还能通过其贝叶斯特性量化预测不确定性。该框架在分布外场景下仍能生成可靠的物理一致性预测。
原文摘要 · Abstract (English)
The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state prediction in scenarios where the underlying dynamics are unknown. However, the performance, reliability, and uncertainty of data-driven predictors become compromised when encountering out-of-distribution observations relative to the training data. In this paper, we introduce a Plug-and-Play Physics-Informed Machine Learning (PnP-PIML) framework to address this challenge. Our method employs conformal prediction to identify outlier dynamics and, in that case, switches from a nominal predictor to a physics-consistent model, namely distributed Port-Hamiltonian systems (dPHS). We leverage Gaussian processes to model the energy function of the dPHS, enabling not only the learning of system dynamics but also the quantification of predictive uncertainty through its Bayesian nature. In this way, the proposed framework produces reliable physics-informed predictions even for the out-of-distribution scenarios.
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