Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration

TL;DR

This paper investigates an active learning algorithm for Gaussian Process regression in autonomous robotic exploration, emphasizing cost-aware decision-making to optimize data collection efficiency under resource constraints.

Contribution

It introduces a cost-aware active learning approach for Gaussian Process regression and evaluates its performance in terrain mapping tasks.

Findings

Traditional uncertainty metrics with distance constraints outperform cost-sensitive policies in minimizing RMSE.

Cost-aware policies do not inherently optimize information gain relative to travel distance.

Incorporating action cost provides insights for efficient exploration under resource limitations.

Abstract

In missions constrained by finite resources, efficient data collection is critical. Informative path planning, driven by automated decision-making, optimizes exploration by reducing the costs associated with accurate characterization of a target in an environment. Previous implementations of active learning did not consider the action cost for regression problems or only considered the action cost for classification problems. This paper analyzes an AL algorithm for Gaussian Process regression while incorporating action cost. The algorithm's performance is compared on various regression problems to include terrain mapping on diverse simulated surfaces along metrics of root mean square error, samples and distance until convergence, and model variance upon convergence. The cost-dependent acquisition policy doesn't organically optimize information gain over distance. Instead, the traditional uncertainty metric with a distance constraint best minimizes root-mean-square error over trajectory distance. This studys impact is to provide insight into incorporating action cost with AL methods to optimize exploration under realistic mission constraints.