# Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem

arXiv: https://arxiv.org/abs/2607.16084 · HTML: https://mlsanity.com/paper/2607.16084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [The Scenario Approach for Stochastic Model Predictive Control with Bounds on Closed-Loop Constraint Violations](https://mlsanity.com/paper/1307.5640/) (2013)
- [Compression, Generalization and Learning](https://mlsanity.com/paper/2301.12767/) (2023)
- [Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees](https://mlsanity.com/paper/2512.04781/) (2025)
- [BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagation](https://mlsanity.com/paper/2312.15521/) (2023)
- [The Implicit Rigid Tube Model Predictive Control](https://mlsanity.com/paper/2306.14543/) (2023)
- [Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees](https://mlsanity.com/paper/2403.04655/) (2024)
- [Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty](https://mlsanity.com/paper/2601.01940/) (2026)

## Abstract (arXiv)

This paper illustrates the Pick-to-Learn methodology applied to the calibration of a Model Predictive Control policy. While developed around a specific example, the presentation is meant to highlight a methodology of broad applicability. The example concerns an aircraft traveling from an origin point to a destination point in the presence of uncertain crosswinds and a low-connectivity zone that should be avoided. The MPC policy is parameterized by two hyperparameters, which are selected from data by the P2L procedure. Starting from a dataset of 400 wind realizations, also called scenarios, P2L identifies a final compression set containing only two informative scenarios. The resulting MPC policy avoids the low-connectivity zone on all available scenarios and, according to the P2L theory, satisfies a probabilistic risk bound of 4.8% at confidence level 1-10-5, where the risk is the probability of entering the low-connectivity zone in a future flight under a new wind realization not included in the sample.