Learning Implicit Priors for Motion Optimization

TL;DR

This paper explores how Energy-based Models can serve as flexible, data-driven priors in motion optimization, enhancing robot manipulation by integrating learned probabilistic distributions.

Contribution

It introduces modeling choices and architectures for adapting EBMs into motion optimization, enabling their use as guiding priors in robotic manipulation tasks.

Findings

EBMs can be effectively integrated into motion optimization.

Regularizers improve EBM performance with gradient-based optimizers.

Experimental results demonstrate benefits in simulated and real robot tasks.

Abstract

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.