Diffusion Policies for Generative Modeling of Spacecraft Trajectories

Despite its promise and the tremendous advances in nonlinear optimization solvers in recent years, trajectory optimization has primarily been constrained to offline usage due to the limited compute capabilities of radiation hardened flight computers [3]. However, with a flurry of proposed mission concepts that call for increasingly greater on-board autonomy [4], bridging this gap in the state-of-practice is necessary to allow for scaling current trajectory design techniques for future missions. Recently, researchers have turned to machine learning and data-driven techniques as a promising method for reducing the runtimes necessary for solving challenging constrained optimization problems [5, 6]. Such approaches entail learning what is known as the problem-to-solution mapping between the problem parameters that vary between repeated instances of solving the trajectory optimization problem to the full optimization solution and these works typically use a Deep Neural Network (DNN) to model this mapping [7-9]. Given parameters of new instances of the trajectory optimization problem, this problem-to-solution mapping can be used online to yield candidate trajectories to warm start the nonlinear optimization solver and this warm start can enable significant solution speed ups. One shortcoming of these aforementioned data-driven approaches is that they have limited scope of use and the learned problem-to-solution mapping only applies for one specific trajectory optimization formulation. With a change to the mission design specifications that yields, e.g., a different optimization constraint, a new problem-to-solution mapping has to be learned offline and this necessitates generating a new dataset of solved trajectory optimization problems. To this end, our work explores the use of compositional diffusion modeling to allow for generalizable learning of the problem-to-solution mapping and equip mission designers with the ability to interleave different learned models to satisfy a rich set of trajectory design specifications. Compositional diffusion modeling enables training of a model to both sample and plan from.