RapidDOC: Riccati-Preconditioned Implicit Differentiation for Differentiable Optimal Control under Operator Mismatch

Abstract

Differentiable optimal control embeds constrained optimization problems in data-driven pipelines by differentiating their optimality conditions and propagating task-loss gradients to model or objective parameters. A key observation is that forward optimization and backward sensitivity computation involve related stage-structured linear systems, making reuse of forward factorization information natural. However, direct reuse is generally invalid, because differences in linearization, barrier terms, regularization, and curvature can create a mismatch between the retained forward operator and the target operator defining the derivative. We therefore propose RapidDOC, a factorization-reuse method that leaves the target adjoint equation unchanged. The retained cache produces a candidate adjoint and, when its target residual exceeds the prescribed tolerance, serves as a fixed right preconditioner for GMRES. A cache-action error operator characterizes directional exactness, the preconditioned iteration, and the relation between target residual and outer-gradient error. An ablation on three constrained robotic OCPs shows that the proposed method achieves lower backward time than both target-system refactorization and a no-reuse iterative method, demonstrating the computational benefit of reusing the retained forward factorizations. Across open- and closed-loop parameter-learning experiments, the proposed method achieves learning performance comparable to the baselines, while yielding the lowest open-loop backward time on all three tasks and reducing total closed-loop solver-call time by 40.5-83.1% relative to the fastest competing method. Finally, deploying the learned controller on a physical vehicle reduces the median figure-eight tracking RMSE from 22.75 to 6.09 cm, demonstrating its potential for hardware deployment.