Diffusion Models for Generating Ballistic Spacecraft Trajectories

Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks such as image generation, multivariate time series forecasting, and robotic trajectory planning. We further analyze the model's ability to learn the characteristics of the original dataset and its ability to produce transfers that follow the underlying dynamics. Ablation studies were conducted to determine how model performance varies with model size and trajectory temporal resolution. In addition, a performance benchmark is designed to assess the generative model's usefulness for trajectory design, conduct model performance comparisons, and lay the groundwork for evaluating different generative models for trajectory design beyond diffusion. The results of this analysis showcase several useful properties of diffusion models that, when taken together, can enable a future system for generative trajectory design powered by diffusion models. INTRODUCTION Diffusion models are a type of generative model that have achieved state-of-the-art performance across creative and scientific domains. Concerning trajectory design, diffusion models have shown promising results in robotics. Janner et al. propose combining diffusion models with reinforcement learning techniques to develop flexible trajectory planning strategies.