By approximating posterior distributions with weighted samples, particle filters (PFs) provide
an efficient mechanism for solving non-linear sequential state estimation problems. While …

Bayesian models are powerful tools for studying complex data, allowing the analyst to
encode rich hierarchical dependencies and leverage prior information. Most importantly …

This paper introduces a novel state estimation framework for robots using differentiable
ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble …

Differentiable Filters are recursive Bayesian estimators that derive the state transition and
measurement models from data alone. Their data-driven nature eschews the need for …

It has been widelydocumented that the sampling and resampling steps in particle filters
cannot be differentiated. The reparameterisation trick was introduced to allow the sampling …

This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a
differentiable filter architecture. The proposed approach enables end-to-end training to learn …

Accurate estimation of the states of a nonlinear dynamical system is crucial for their design,
synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories …

Time-series datasets are central in numerous fields of science and engineering, such as
biomedicine, Earth observation, and network analysis. Extensive research exists on state …

Differentiable particle filters are an emerging class of particle filtering methods that use
neural networks to construct and learn parametric state-space models. In real-world …

Tuning of measurement models is challenging in real-world applications of sequential
Monte Carlo methods. Re-cent advances in differentiable particle filters have led to various …