mlsanityData to 14 Oct 2026

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

arXiv 2312.06810 ↗First posted 11 Dec 2023cs.RO cs.LG cs.SYcited by 5

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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.

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

18 of 26 references.

References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2022Planning-oriented Autonomous Drivingcited by 1,087
  4. 2020Predicting Semantic Map Representations from Images using Pyramid Occupancy Networkscited by 175
  5. 2020Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programmingcited by 43
  6. 2016Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processescited by 39
  7. 2020Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functionscited by 33
  8. 2016Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomycited by 28
  9. 2021Safe Control with Neural Network Dynamic Modelscited by 22
  10. 2019Robust Guarantees for Perception-Based Controlcited by 20
  11. 2022Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fieldscited by 16
  12. 2016Learning Quadrotor Dynamics Using Neural Network for Flight Controlcited by 15
6 more
  1. 2018Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squarescited by 15
  2. 2020Certainty Equivalent Perception-Based Controlcited by 11
  3. 2013Barrier Certificates Revisitedcited by 2
  4. 2021Set-theoretic Localization for Mobile Robots with Infrastructure-based Sensingcited by 1
  5. 2021SeanNet: Semantic Understanding Network for Localization Under Object Dynamicscited by 1
  6. 2023Model Predictive Control of Diesel Engine Emissions Based on Neural Network Modelingcited by 1

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Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2025Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attackscited by 20
  2. 2024Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Controlcited by 5
  3. 2024Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximationcited by 5
  4. 2025Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Controlcited by 0
  5. 2025Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structurescited by 0