Ising Machines for Model Predictive Path Integral-Based Optimal Control

TL;DR

This paper introduces an innovative approach to Model Predictive Control by leveraging Ising machines to efficiently solve the underlying optimization problem, enabling real-time control in robotics.

Contribution

It presents a novel formulation of MPC as a QUBO problem mapped onto an Ising model, facilitating probabilistic computing for control tasks.

Findings

Achieves accurate trajectory tracking comparable to traditional MPPI.

Demonstrates real-time control potential in robotics applications.

Maps MPC onto an energy landscape suitable for Gibbs sampling.

Abstract

We present a sampling-based Model Predictive Control (MPC) method that implements Model Predictive Path Integral (MPPI) as an \emph{Ising machine}, suitable for novel forms of probabilistic computing. By expressing the control problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we map MPC onto an energy landscape suitable for Gibbs sampling from an Ising model. This formulation enables efficient exploration of (near-)optimal control trajectories. We demonstrate that the approach achieves accurate trajectory tracking compared to a reference MPPI implementation, highlighting the potential of Ising-based MPPI for real-time control in robotics and autonomous systems.