Learning GP-BayesFilters via Gaussian process latent variable models

Abstract

GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters.GP-BayesFilters learn nonparametric filter models from training data containing sequences of control inputs, observations, and ground truth states.The need for ground truth states limits the applicability of GP-BayesFilters to systems for which the ground truth can be estimated without prohibitive overhead.In this paper we introduce GPBF-LEARN, a framework for training GP-BayesFilters without any ground truth states.Our approach extends Gaussian Process Latent Variable Models to the setting of dynamical robotics systems.We show how weak labels for the ground truth states can be incorporated into the GPBF-LEARN framework.The approach is evaluated using a difficult tracking task, namely tracking a slotcar based on IMU measurements only.