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Optimizing Trajectories with Closed-Loop Dynamic SQP

Sumeet Singh
Jean-Jacques Slotine
ICRA (2022)
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Abstract

Indirect trajectory optimization methods such as Differential Dynamic Programming (DDP) have found considerable success when only planning under dynamic feasibility constraints. Meanwhile, nonlinear programming (NLP) has been the state-of-the-art approach when faced with additional constraints (e.g., control bounds, obstacle avoidance). However, a na{\"i}ve implementation of NLP algorithms, e.g., shooting-based sequential quadratic programming (SQP), may suffer from slow convergence -- caused from natural instabilities of the underlying system manifesting as poor numerical stability within the optimization. Re-interpreting the DDP closed-loop rollout policy as a \emph{sensitivity-based correction to a second-order search direction}, we demonstrate how to compute analogous closed-loop policies (i.e., feedback gains) for \emph{constrained} problems. Our key theoretical result introduces a novel dynamic programming-based constraint-set recursion that augments the canonical ``cost-to-go" backward pass. On the algorithmic front, we develop a hybrid-SQP algorithm incorporating DDP-style closed-loop rollouts, enabled via efficient \emph{parallelized} computation of the feedback gains. Finally, we validate our theoretical and algorithmic contributions on a set of increasingly challenging benchmarks, demonstrating significant improvements in convergence speed over standard open-loop SQP.

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