# Local Bayesian Optimization for Controller Tuning with Crash Constraints

arXiv: https://arxiv.org/abs/2411.16267 · HTML: https://mlsanity.com/paper/2411.16267/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Local policy search with Bayesian optimization](https://mlsanity.com/paper/2106.11899/) (2021)
- [A survey on policy search algorithms for learning robot controllers in a handful of trials](https://mlsanity.com/paper/1807.02303/) (2018)
- [Automatic LQR Tuning Based on Gaussian Process Global Optimization](https://mlsanity.com/paper/1605.01950/) (2016)
- [Local Bayesian optimization via maximizing probability of descent](https://mlsanity.com/paper/2210.11662/) (2022)
- [GoSafe: Globally Optimal Safe Robot Learning](https://mlsanity.com/paper/2105.13281/) (2021)
- [Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning](https://mlsanity.com/paper/1906.12086/) (2019)
- [Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization](https://mlsanity.com/paper/2002.02820/) (2020)
- [The Behavior and Convergence of Local Bayesian Optimization](https://mlsanity.com/paper/2305.15572/) (2023)
- [Robot Learning with Crash Constraints](https://mlsanity.com/paper/2010.08669/) (2020)
- [Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization](https://mlsanity.com/paper/2010.15211/) (2020)
- [On Controller Tuning with Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2207.11120/) (2022)
- [Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization](https://mlsanity.com/paper/1807.08855/) (2018)
- [C-GLISp: Preference-Based Global Optimization under Unknown Constraints with Applications to Controller Calibration](https://mlsanity.com/paper/2106.05639/) (2021)
- [Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization](https://mlsanity.com/paper/2005.03970/) (2020)
- [Benchmark of Bayesian Optimization and Metaheuristics for Control Engineering Tuning Problems with Crash Constraints](https://mlsanity.com/paper/2211.02571/) (2022)
- [Robust Parametrization of a Model Predictive Controller for a CNC Machining Center Using Bayesian Optimization](https://mlsanity.com/paper/2010.06869/) (2020)
- [Joint Constrained Bayesian Optimization of Planning, Guidance, Control, and State Estimation of an Autonomous Underwater Vehicle](https://mlsanity.com/paper/2205.14669/) (2022)
- [What is the Best Way to Optimally Parameterize the MPC Cost Function for Vehicle Guidance?](https://mlsanity.com/paper/2212.06561/) (2022)

## Built on by (most cited first, 3 of 3)

- [BayeSQP: Bayesian Optimization through Sequential Quadratic Programming](https://mlsanity.com/paper/2602.03232/) (2026)
- [SLO-Guard: Crash-Aware, Budget-Consistent Autotuning for SLO-Constrained LLM Serving](https://mlsanity.com/paper/2604.17627/) (2026)
- [Local Preferential Bayesian Optimization](https://mlsanity.com/paper/2606.02351/) (2026)

## Abstract (arXiv)

Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local variant of BO to include crash constraints, where the controller can only be successfully evaluated in an a-priori unknown feasible region. We demonstrate the efficiency of the proposed method through simulations and hardware experiments. Our findings showcase the potential of local BO to enhance controller performance and reduce the time and resources necessary for tuning.