Abstract
Learning dynamical models from high dimensional time series data is an crucial research topic for a variety of applications. Kalman Variational Auto-Encoders (KVAE) is one of the recent methods, which is composed of VAE and Linear Gaussian State Space Models (LGSSM). In this paper, the predictive performance of KVAE is investigated with control inputs through a 2DOF robot arm reaching task in simulations.
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