Equality Constrained Diffusion for Direct Trajectory Optimization

TL;DR

This paper introduces a novel diffusion-based trajectory optimization method that enforces nonlinear equality constraints, enabling direct trajectory optimization with benefits like flexible constraints and reduced sensitivity.

Contribution

The paper presents the first diffusion-based algorithm capable of handling nonlinear equality constraints for direct trajectory optimization.

Findings

Enables direct trajectory optimization with nonlinear constraints.

Supports dynamic feasibility enforcement without rollouts.

Improves flexibility and numerical stability in control systems.

Abstract

The recent success of diffusion-based generative models in image and natural language processing has ignited interest in diffusion-based trajectory optimization for nonlinear control systems. Existing methods cannot, however, handle the nonlinear equality constraints necessary for direct trajectory optimization. As a result, diffusion-based trajectory optimizers are currently limited to shooting methods, where the nonlinear dynamics are enforced by forward rollouts. This precludes many of the benefits enjoyed by direct methods, including flexible state constraints, reduced numerical sensitivity, and easy initial guess specification. In this paper, we present a method for diffusion-based optimization with equality constraints. This allows us to perform direct trajectory optimization, enforcing dynamic feasibility with constraints rather than rollouts. To the best of our knowledge, this is the first diffusion-based optimization algorithm that supports the general nonlinear equality constraints required for direct trajectory optimization.