Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systems

TL;DR

This paper presents a novel multi-fidelity residual physics-informed neural process approach for real-time robotic state estimation, effectively handling model mismatch and providing reliable uncertainty guarantees in safety-critical applications.

Contribution

It introduces a real-time, data-driven estimation method combining multi-fidelity learning with residual physics-informed neural processes and conformal prediction for uncertainty quantification.

Findings

Outperforms traditional Kalman filters in real-time estimation accuracy.

Provides reliable uncertainty bounds for model predictions.

Demonstrates viability in robotic system state estimation.

Abstract

Various neural network architectures are used in many of the state-of-the-art approaches for real-time nonlinear state estimation. With the ever-increasing incorporation of these data-driven models into the estimation domain, model predictions with reliable margins of error are a requirement -- especially for safety-critical applications. This paper discusses the application of a novel real-time, data-driven estimation approach based on the multi-fidelity residual physics-informed neural process (MFR-PINP) toward the real-time state estimation of a robotic system. Specifically, we address the model-mismatch issue of selecting an accurate kinematic model by tasking the MFR-PINP to also learn the residuals between simple, low-fidelity predictions and complex, high-fidelity ground-truth dynamics. To account for model uncertainty present in a physical implementation, robust uncertainty guarantees from the split conformal (SC) prediction framework are modeled in the training and inference paradigms. We provide implementation details of our MFR-PINP-based estimator for a hybrid online learning setting to validate our model's usage in real-time applications. Experimental results of our approach's performance in comparison to the state-of-the-art variants of the Kalman filter (i.e. unscented Kalman filter and deep Kalman filter) in estimation scenarios showed promising results for the MFR-PINP model as a viable option in real-time estimation tasks.