4.5 Article

How to train your differentiable filter

Related references

Note: Only part of the references are listed.
Article Robotics

Combining learned and analytical models for predicting action effects from sensory data

Alina Kloss et al.

Summary: This work explores the advantages and limitations of neural-network-based learning approaches for predicting the effects of physical interactions. It shows how analytical and learned models can be combined to leverage their respective strengths. A systematic evaluation on a large real-world dataset reveals that the hybrid architecture reduces required training data and improves generalization to novel physical interactions.

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2022)

Article Automation & Control Systems

Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter

Vinay A. Bavdekar et al.

JOURNAL OF PROCESS CONTROL (2011)

Article Engineering, Chemical

Systematic estimation of state noise statistics for extended Kalman filters

J Valappil et al.

AICHE JOURNAL (2000)