Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models

arXiv:2308.01557v2 Announce Type: replace-cross
Abstract: Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we propose learning diffusion models as priors. We then …

abstract arxiv bootstrapping cs.ai cs.lg cs.ro diffusion diffusion models generative generative models motion planning optimization planning prior robot trajectory type