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This paper introduces a novel control framework that combines conformal prediction (CP) and system level synthesis (SLS) to achieve robust out-of-distribution (OOD) planning and control with learned dynamics models. The method uses weighted CP with a learned covariance model to derive high-confidence model error bounds, which are then incorporated into an SLS-based robust nonlinear MPC formulation with volume-optimized reachable sets for constraint tightening. Empirical results on nonlinear systems like a 4D car and a 12D quadcopter demonstrate improved safety and robustness, particularly in OOD scenarios, compared to baselines.
Guarantee safety when your robot ventures into the unknown by using conformal prediction to generate high-confidence error bounds for learned dynamics models, then tightening constraints in a Model Predictive Controller.
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensuring safety and robustness when using learned dynamics models beyond the training data distribution. We first derive high-confidence model error bounds using weighted CP with a learned, state-control-dependent covariance model. These bounds are integrated into an SLS-based robust nonlinear model predictive control (MPC) 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 method on nonlinear systems of increasing complexity, including a 4D car and a {12D} quadcopter, improving safety and robustness compared to fixed-bound and non-robust baselines, especially outside of the data distribution.
