Tootfinder

This https://arxiv.org/abs/2308.01557 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

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

Fast Bootstrapping Nonparametric Maximum Likelihood for Latent Mixture Models
Shijie Wang, Minsuk Shin, Ray Bai
https://arxiv.org/abs/2402.18748 https://…

Fast Bootstrapping Nonparametric Maximum Likelihood for Latent Mixture Models
Estimating the mixing density of a latent mixture model is an important task in signal processing. Nonparametric maximum likelihood estimation is one popular approach to this problem. If the latent variable distribution is assumed to be continuous, then bootstrapping can be used to approximate it. However, traditional bootstrapping requires repeated evaluations on resampled data and is not scalable. In this letter, we construct a generative process to rapidly produce nonparametric maximum likel…