Parareal with a Learned Coarse Model for Robotic Manipulation

TL;DR

This paper introduces a neural network-based coarse model for the Parareal algorithm, significantly accelerating physics predictions in robotic manipulation and enabling applications with multiple objects, both in simulation and real-world scenarios.

Contribution

It demonstrates that a learned neural network coarse model improves Parareal convergence and applicability over traditional physics-based models in robotic manipulation tasks.

Findings

Learned coarse model accelerates Parareal convergence.

Enables Parareal to handle multi-object scenarios.

Achieves accurate real-world physics predictions in robotic pushing.

Abstract

A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, we conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Videos are at https://youtu.be/wCh2o1rf-gA.