# Towards Safe Path Tracking Using the Simplex Architecture

arXiv: https://arxiv.org/abs/2503.10559 · HTML: https://mlsanity.com/paper/2503.10559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 10 of 10)

- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [The Marathon 2: A Navigation System](https://mlsanity.com/paper/2003.00368/) (2020)
- [From the Desks of ROS Maintainers: A Survey of Modern & Capable Mobile Robotics Algorithms in the Robot Operating System 2](https://mlsanity.com/paper/2307.15236/) (2023)
- [Regulated Pure Pursuit for Robot Path Tracking](https://mlsanity.com/paper/2305.20026/) (2023)
- [Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy](https://mlsanity.com/paper/2306.12369/) (2023)
- [DualGuard MPPI: Safe and Performant Optimal Control by Combining Sampling-Based MPC and Hamilton-Jacobi Reachability](https://mlsanity.com/paper/2502.01924/) (2025)
- [Switching control for incremental stabilization of nonlinear systems via contraction theory](https://mlsanity.com/paper/1510.08368/) (2015)
- [Safety Embedded Control of Nonlinear Systems via Barrier States](https://mlsanity.com/paper/2102.10253/) (2021)
- [Necessary and Sufficient Conditions for Stability of Discrete-Time Switched Linear Systems with Ranged Dwell Time](https://mlsanity.com/paper/2105.14113/) (2021)

## Abstract (arXiv)

Robot navigation in complex environments necessitates controllers that prioritize safety while remaining performant and adaptable. Traditional controllers like Regulated Pure Pursuit, Dynamic Window Approach, and Model-Predictive Path Integral, while reliable, struggle to adapt to dynamic conditions. Reinforcement Learning offers adaptability but state-wise safety guarantees remain challenging and often absent in practice. To address this, we propose a path tracking controller leveraging the Simplex architecture. It combines a Reinforcement Learning controller for adaptiveness and performance with a high-assurance controller providing safety and stability. Our main goal is to provide a safe testbed for the design and evaluation of path-planning algorithms, including machine-learning-based planners. Our contribution is twofold. We firstly discuss general stability and safety considerations for designing controllers using the Simplex architecture. Secondly, we present a Simplex-based path tracking controller. Our simulation results, supported by preliminary in-field tests, demonstrate the controller's effectiveness in maintaining safety while achieving comparable performance to state-of-the-art methods.