Efficient Active Search Via Amortized Path-Integral Policies

Abstract

This work presents amortized path-integral policies that enable efficient and real-time active search for robotic systems. We model search as an active sensing problem where agents select actions to maximize information about target locations. Unlike previous approaches that only consider information gain at final waypoints, our method accounts for observations along entire paths. To address the computational expense of path-integral policies, we amortize costs through Graph Neural Network (GNN) policies trained via behavior cloning. GNNs provide equivariance to spatial transformations and generalize across diverse maps. We validate our approach through field experiments in a 75,000 m² forested environment using an autonomous ground vehicle, along with simulated testing. Our experiments demonstrate successful policy amortization, cross-map transfer, and improved search efficiency.