mlsanityData to 14 Oct 2026

Performance-driven Constrained Optimal Auto-Tuner for MPC

arXiv 2503.07127 ↗First posted 10 Mar 2025cs.LG cs.ROcited by 6

Not on the map. Why?

Abstract (arXiv)

A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we propose a novel method, COAT-MPC, Constrained Optimal Auto-Tuner for MPC. With every tuning iteration, COAT-MPC gathers performance data and learns by updating its posterior belief. It explores the tuning parameters' domain towards optimistic parameters in a goal-directed fashion, which is key to its sample efficiency. We theoretically analyze COAT-MPC, showing that it satisfies performance constraints with arbitrarily high probability at all times and provably converges to the optimum performance within finite time. Through comprehensive simulations and comparative analyses with a hardware platform, we demonstrate the effectiveness of COAT-MPC in comparison to classical Bayesian Optimization (BO) and other state-of-the-art methods. When applied to autonomous racing, our approach outperforms baselines in terms of constraint violations and cumulative regret over time.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

21 of 30 references.

References · 21

  1. 2018A Tutorial on Bayesian Optimizationcited by 744
  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 374
  3. 2017On Kernelized Multi-armed Banditscited by 352
  4. 2020On Information Gain and Regret Bounds in Gaussian Process Banditscited by 158
  5. 2016Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Roboticscited by 134
  6. 2018Maximizing acquisition functions for Bayesian optimizationcited by 114
  7. 2015Safe Controller Optimization for Quadrotors with Gaussian Processescited by 78
  8. 2019Safe Exploration for Interactive Machine Learningcited by 51
  9. 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 50
  10. 2018A survey on policy search algorithms for learning robot controllers in a handful of trialscited by 35
  11. 2019AMZ Driverless: The Full Autonomous Racing Systemcited by 29
  12. 2023Submodular Reinforcement Learningcited by 23
9 more
  1. 2020A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimizationcited by 16
  2. 2020Cautious Bayesian Optimization for Efficient and Scalable Policy Searchcited by 12
  3. 2022Near-Optimal Multi-Agent Learning for Safe Coverage Controlcited by 11
  4. 2024Safe Guaranteed Exploration for Non-linear Systemscited by 8
  5. 2021Contextual Tuning of Model Predictive Control for Autonomous Racingcited by 7
  6. 2021AutoTune: Controller Tuning for High-Speed Flightcited by 5
  7. 2022Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and controlcited by 5
  8. 2022Weighted Maximum Likelihood for Controller Tuningcited by 3
  9. 2025Lipschitz Safe Bayesian Optimization for Automotive Controlcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamicscited by 5
  2. 2025Safe and Near-Optimal Control with Online Dynamics Learningcited by 1
  3. 2025Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Controlcited by 0
  4. 2026SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Taskscited by 0
  5. 2026Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigationcited by 0
  6. 2026Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyondcited by 0