Enhancing State Estimation in Robots: A Data-Driven Approach with Differentiable Ensemble Kalman Filters
Xiao Liu,Geoffrey Clark,Joseph Campbell,Yifan Zhou,Heni Ben Amor,Xiao Liu,Geoffrey Clark,Joseph Campbell,Yifan Zhou,Heni Ben Amor
This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process noise implicitly. Our work is an extension of previous research on differentiable filters, which has provided a strong foundation for our modular and ...
