Trajectory Design and Budgeted Querying for Digital Twin Calibration

2026-08-09

AI Summary

This study addresses the challenge of high interaction data acquisition costs in digital twin calibration by proposing a novel framework that jointly optimizes trajectory generation and parameter querying under limited budgets. The approach explicitly treats trajectory design and query allocation as coupled optimization variables, integrating an incentive-driven reinforcement learning controller, a GRU-based recurrent parameter estimator that incorporates predictive uncertainty, and a budget-aware query strategy. Experimental results demonstrate significant improvements in calibration efficiency and accuracy under data scarcity: on the Pend日晚间 task, only three queries reduce the terminal parameter error to 0.0092 from an uncalibrated baseline of 0.2031; in the Waterworld environment, the online normalized error stabilizes at 4–5%, underscoring the method’s effectiveness in resource-constrained settings.

Abstract

Digital-twin calibration requires interaction data that is expensive to collect. We study two acquisition decisions: which trajectories to generate, and when to spend a limited budget on privileged parameter measurements. Our framework couples an excitation-oriented reinforcement learning controller, a recurrent parameter estimator with predictive uncertainty, and a budgeted query policy. In Pendulum, a Random Forest diagnostic recovers gravity only weakly from task-oriented trajectories and does not recover mass or length, while a GRU trained on excitation-oriented trajectories reaches a mean absolute error of 0.0066 with no queries. We then withdraw continuous oracle access partway through an episode, so that the twin must run on the estimator's output for the remainder. The estimator-plus-policy pipeline achieves a terminal error of 0.0092 under a three-query budget, against 0.2031 for an uncalibrated twin. In partially observable Waterworld, five controllers produce different observed error profiles across three hidden parameters, and an estimator trained on a five-controller mixture reaches online normalized errors of roughly 4-5%. These exploratory case studies are not controlled ablations, but they motivate treating trajectory design and query allocation as explicit design variables in data-scarce calibration.

Problem

• Digital Twin Calibration
• Trajectory Design
• Budgeted Querying
• Data Scarcity
• Parameter Estimation

Innovation

• trajectory design
• budgeted querying
• digital twin calibration
• excitation-oriented control
• recurrent parameter estimation