Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis

Anutam Srinivasan, Antoine Leeman, Glen Chou
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:412-439, 2026.

Abstract

We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS). Our method addresses the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence bounds on model errors using weighted conformal prediction with a learned, state-control-dependent covariance model. These bounds are then integrated into an SLS-based robust nonlinear model predictive control (RMPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our approach on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, showing improved safety and reliability compared to fixed-bound and non-robust baselines, especially outside of the collected data distribution.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-srinivasan26a, title = {Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis}, author = {Srinivasan, Anutam and Leeman, Antoine and Chou, Glen}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {412--439}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/srinivasan26a/srinivasan26a.pdf}, url = {https://proceedings.mlr.press/v331/srinivasan26a.html}, abstract = {We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS). Our method addresses the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence bounds on model errors using weighted conformal prediction with a learned, state-control-dependent covariance model. These bounds are then integrated into an SLS-based robust nonlinear model predictive control (RMPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our approach on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, showing improved safety and reliability compared to fixed-bound and non-robust baselines, especially outside of the collected data distribution.} }
Endnote
%0 Conference Paper %T Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis %A Anutam Srinivasan %A Antoine Leeman %A Glen Chou %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-srinivasan26a %I PMLR %P 412--439 %U https://proceedings.mlr.press/v331/srinivasan26a.html %V 331 %X We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS). Our method addresses the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence bounds on model errors using weighted conformal prediction with a learned, state-control-dependent covariance model. These bounds are then integrated into an SLS-based robust nonlinear model predictive control (RMPC) formulation, which performs constraint tightening over the prediction horizon via volume-optimized forward reachable sets. We provide theoretical guarantees on coverage and robustness under distributional drift, and analyze the impact of data density and trajectory tube size on prediction coverage. Empirically, we demonstrate our approach on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, showing improved safety and reliability compared to fixed-bound and non-robust baselines, especially outside of the collected data distribution.
APA
Srinivasan, A., Leeman, A. & Chou, G.. (2026). Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:412-439 Available from https://proceedings.mlr.press/v331/srinivasan26a.html.

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