  In this paper, we focus on the problem of integrating Energy-based Models
(EBM) as guiding priors for motion optimization. EBMs are a set of neural
networks that can represent expressive probability density distributions in
terms of a Gibbs distribution parameterized by a suitable energy function. Due
to their implicit nature, they can easily be integrated as optimization factors
or as initial sampling distributions in the motion optimization problem, making
them good candidates to integrate data-driven priors in the motion optimization
problem. In this work, we present a set of required modeling and algorithmic
choices to adapt EBMs into motion optimization. We investigate the benefit of
including additional regularizers in the learning of the EBMs to use them with
gradient-based optimizers and we present a set of EBM architectures to learn
generalizable distributions for manipulation tasks. We present multiple cases
in which the EBM could be integrated for motion optimization and evaluate the
performance of learned EBMs as guiding priors for both simulated and real robot
experiments.
