11 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…
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…
Algorithm Design and Comparative Test of Natural Gradient Gaussian Approximation Filter
Wenhan Cao, Tianyi Zhang, Shengbo Eben Li
Popular Bayes filters typically rely on linearization techniques such as Taylor series expansion and stochastic linear regression to use the structure of standard Kalman filter. Th…
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
Feihong Zhang, Guojian Zhan, Bin Shuai +3
Reinforcement learning (RL), known for its self-evolution capability, offers a promising approach to training high-level autonomous driving systems. However, handling constraints r…
NANO-SLAM : Natural Gradient Gaussian Approximation for Vehicle SLAM
Tianyi Zhang, Wenhan Cao, Chang Liu +3
Accurate localization is a challenging task for autonomous vehicles, particularly in GPS-denied environments such as urban canyons and tunnels. In these scenarios, simultaneous loc…
Robust State Estimation for Legged Robots with Dual Beta Kalman Filter
Tianyi Zhang, Wenhan Cao, Chang Liu +3
Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large…
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…
Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers
Shiqi Liu, Wenhan Cao, Chang Liu +2
Multi-object tracking (MOT) is an essential technique for navigation in autonomous driving. In tracking-by-detection systems, biases, false positives, and misses, which are referre…