Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees

Devesh Nath, Haoran Yin, Glen Chou
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:71-97, 2026.

Abstract

We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics, enabling fast and scalable closed-loop reachability analysis in the lifted space. We design a linear tracking controller in this space for a given reference trajectory, and map the resulting reachable set back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically-valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user-specified probability. These bounds generalize across references, enabling reuse without re-computation. Results on high-dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-nath26a, title = {Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees}, author = {Nath, Devesh and Yin, Haoran and Chou, Glen}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {71--97}, 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/nath26a/nath26a.pdf}, url = {https://proceedings.mlr.press/v331/nath26a.html}, abstract = {We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics, enabling fast and scalable closed-loop reachability analysis in the lifted space. We design a linear tracking controller in this space for a given reference trajectory, and map the resulting reachable set back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically-valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user-specified probability. These bounds generalize across references, enabling reuse without re-computation. Results on high-dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods.} }
Endnote
%0 Conference Paper %T Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees %A Devesh Nath %A Haoran Yin %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-nath26a %I PMLR %P 71--97 %U https://proceedings.mlr.press/v331/nath26a.html %V 331 %X We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics, enabling fast and scalable closed-loop reachability analysis in the lifted space. We design a linear tracking controller in this space for a given reference trajectory, and map the resulting reachable set back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically-valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user-specified probability. These bounds generalize across references, enabling reuse without re-computation. Results on high-dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods.
APA
Nath, D., Yin, H. & Chou, G.. (2026). Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:71-97 Available from https://proceedings.mlr.press/v331/nath26a.html.

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