DualGuard MPPI: Safe and Performant Optimal Control by Combining Sampling-Based MPC and Hamilton-Jacobi Reachability

TL;DR

DualGuard-MPPI combines sampling-based MPC with Hamilton-Jacobi reachability to ensure safety and improve performance in optimal control tasks, demonstrated through simulations and hardware tests.

Contribution

It introduces a novel framework integrating reachability analysis into MPPI to enforce safety constraints while enhancing control performance.

Findings

Achieves higher performance than existing MPPI methods.

Ensures provable safety for all control samples.

Reduces sampling variance to improve exploration.

Abstract

Designing controllers that are both safe and performant is inherently challenging. This co-optimization can be formulated as a constrained optimal control problem, where the cost function represents the performance criterion and safety is specified as a constraint. While sampling-based methods, such as Model Predictive Path Integral (MPPI) control, have shown great promise in tackling complex optimal control problems, they often struggle to enforce safety constraints. To address this limitation, we propose DualGuard-MPPI, a novel framework for solving safety-constrained optimal control problems. Our approach integrates Hamilton-Jacobi reachability analysis within the MPPI sampling process to ensure that all generated samples are provably safe for the system. On the one hand, this integration allows DualGuard-MPPI to enforce strict safety constraints; at the same time, it facilitates a more effective exploration of the environment with the same number of samples, reducing the effective sampling variance and leading to better performance optimization. Through several simulations and hardware experiments, we demonstrate that the proposed approach achieves much higher performance compared to existing MPPI methods, without compromising safety.