Diverse Motion Planning with Stein Diffusion Trajectory Inference

Abstract

Acquiring prior knowledge of trajectory distributions in specific environments can significantly expedite the optimisation process in robot motion planning. Leveraging successful past plans and utilising trajectory generative models as priors offers a clear advantage. Previous studies have proposed various methods to harness these priors, such as using prior samples for initialisation or incorporating the prior distribution into trajectory optimisation through inference. Recently, diffusion models have demonstrated effectiveness in encoding multi-modal data in high-dimensional settings. In this study, we introduce a methodology that integrates Stein Variational Gradient Descent (SVGD) with Gaussian Process Motion Planning (GPMP), leveraging diffusion models as multi-modal priors. This approach combines the advantages of deep generative model and Bayesian inference to reduce the computation time required to approximate the posterior distribution of trajectories, particularly when adapting to new, unseen environments. In addition, we incorporate path signatures into our method to enhance the diversity of the posterior distribution, thereby improving the optimality of trajectories in multi-modal settings. To validate our approach, we conduct comparative assessments against multiple baseline methods across various scenarios, including 2D planar robots and robotic manipulators.