# System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models

arXiv: https://arxiv.org/abs/2312.06810 · HTML: https://mlsanity.com/paper/2312.06810/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming](https://mlsanity.com/paper/2004.07876/) (2020)
- [Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processes](https://mlsanity.com/paper/1603.04915/) (2016)
- [Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions](https://mlsanity.com/paper/2010.16001/) (2020)
- [Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy](https://mlsanity.com/paper/1610.01238/) (2016)
- [Safe Control with Neural Network Dynamic Models](https://mlsanity.com/paper/2110.01110/) (2021)
- [Robust Guarantees for Perception-Based Control](https://mlsanity.com/paper/1907.03680/) (2019)
- [Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fields](https://mlsanity.com/paper/2209.12266/) (2022)
- [Learning Quadrotor Dynamics Using Neural Network for Flight Control](https://mlsanity.com/paper/1610.05863/) (2016)
- [Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squares](https://mlsanity.com/paper/1802.08917/) (2018)
- [Certainty Equivalent Perception-Based Control](https://mlsanity.com/paper/2008.12332/) (2020)
- [Barrier Certificates Revisited](https://mlsanity.com/paper/1310.6481/) (2013)
- [Set-theoretic Localization for Mobile Robots with Infrastructure-based Sensing](https://mlsanity.com/paper/2110.01749/) (2021)
- [SeanNet: Semantic Understanding Network for Localization Under Object Dynamics](https://mlsanity.com/paper/2110.02276/) (2021)
- [Model Predictive Control of Diesel Engine Emissions Based on Neural Network Modeling](https://mlsanity.com/paper/2311.03555/) (2023)

## Built on by (most cited first, 5 of 5)

- [Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks](https://mlsanity.com/paper/2503.00187/) (2025)
- [Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control](https://mlsanity.com/paper/2403.15577/) (2024)
- [Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation](https://mlsanity.com/paper/2404.13456/) (2024)
- [Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control](https://mlsanity.com/paper/2505.11546/) (2025)
- [Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures](https://mlsanity.com/paper/2505.19521/) (2025)

## Abstract (arXiv)

The Neural Network (NN), as a black-box function approximator, has been considered in many control and robotics applications. However, difficulties in verifying the overall system safety in the presence of uncertainties hinder the deployment of NN modules in safety-critical systems. In this paper, we leverage the NNs as predictive models for trajectory tracking of unknown dynamical systems. We consider controller design in the presence of both intrinsic uncertainty and uncertainties from other system modules. In this setting, we formulate the constrained trajectory tracking problem and show that it can be solved using Mixed-integer Linear Programming (MILP). The proposed MILP-based approach is empirically demonstrated in robot navigation and obstacle avoidance through simulations. The demonstration videos are available at https://xiaolisean.github.io/publication/2023-11-01-L4DC2024.