Generative Stochastic Optimal Transport: Guided Harmonic Path-Integral Diffusion

TL;DR

This paper introduces GH-PID, a linear, guided stochastic optimal transport framework that enables stable, interpretable, and geometry-aware trajectory generation with exact terminal distribution matching.

Contribution

GH-PID provides a novel, analytically tractable approach to guided stochastic optimal transport with explicit solutions and interpretability, applicable to multi-scenario navigation tasks.

Findings

Stable sampling and differentiable protocol learning achieved.

Closed-form expressions for GMM terminal laws.

Effective multi-expert trajectory fusion demonstrated.

Abstract

We introduce Guided Harmonic Path-Integral Diffusion (GH-PID), a linearly-solvable framework for guided Stochastic Optimal Transport (SOT) with a hard terminal distribution and soft, application-driven path costs. A low-dimensional guidance protocol shapes the trajectory ensemble while preserving analytic structure: the forward and backward Kolmogorov equations remain linear, the optimal score admits an explicit Green-function ratio, and Gaussian-Mixture Model (GMM) terminal laws yield closed-form expressions. This enables stable sampling and differentiable protocol learning under exact terminal matching. We develop guidance-centric diagnostics -- path cost, centerline adherence, variance flow, and drift effort -- that make GH-PID an interpretable variational ansatz for empirical SOT. Three navigation scenarios illustrated in 2D: (i) Case A: hand-crafted protocols revealing how geometry and stiffness shape lag, curvature effects, and mode evolution; (ii) Case B: single-task protocol learning, where a PWC centerline is optimized to minimize integrated cost; (iii) Case C: multi-expert fusion, in which a commander reconciles competing expert/teacher trajectories and terminal beliefs through an exact product-of-experts law and learns a consensus protocol. Across all settings, GH-PID generates geometry-aware, trust-aware trajectories that satisfy the prescribed terminal distribution while systematically reducing integrated cost.