10 papers
Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems
Chang Liu, Wenhan Cao, Zeju Sun +8
Bayesian filtering is a cornerstone of state estimation in complex systems such as aerospace systems, yet exact solutions are available only for linear Gaussian models. In practice…
Natural Gradient Gaussian Approximation Filter on Lie Groups for Robot State Estimation
Tianyi Zhang, Wenhan Cao, Chang Liu +2
Accurate state estimation for robotic systems evolving on Lie group manifolds, such as legged robots, is a prerequisite for achieving agile control. However, this task is challenge…
Natural Gradient Gaussian Approximation Filter with Positive Definiteness Guarantee
Tianyi Zhang, Wenhan Cao, Shengbo Eben Li
Popular Bayes filters often apply linearization techniques, such as Taylor expansion or stochastic linear regression, to enable the use of the Kalman filter structure, but this can…
Nonlinear Bayesian Filtering with Natural Gradient Gaussian Approximation
Wenhan Cao, Tianyi Zhang, Zeju Sun +3
Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facin…
Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving
Tianze Zhu, Yinuo Wang, Wenjun Zou +6
Reinforcement learning (RL) is a fundamental methodology in autonomous driving systems, where generative policies exhibit considerable potential by leveraging their ability to mode…
One Filters All: A Generalist Filter for State Estimation
Shiqi Liu, Wenhan Cao, Chang Liu +3
Estimating hidden states in dynamical systems, also known as optimal filtering, is a long-standing problem in various fields of science and engineering. In this paper, we introduce…