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20242026
most citedPrimal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

math.OC2026

Dual-Regularized Riccati Recursions for Interior-Point Optimal Control

João Sousa-Pinto, Dominique Orban

We derive closed-form extensions of the sequential and parallel Riccati recursions for solving dual-regularized linear-quadratic regulator (LQR) problems, with sequential ti…

math.OC2026

Quasi-Newton and Krylov Methods for the Solution of Nonconvex Trust-Region Subproblems

Johann Bourhis, Oihan Cordelier, Jean-Pierre Dussault +2

We study the solution of symmetric positive-definite linear systems by way of families of full- and limited-memory methods. Our contributions are threefold. We first derive new rel…

cs.RO20254 cited

Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots

Lorenzo Amatucci, João Sousa-Pinto, Giulio Turrisi +3

This paper introduces a novel Model Predictive Control (MPC) implementation for legged robot locomotion that leverages GPU parallelization. Our approach enables both temporal and s…

math.OC2024

An efficient algorithm for solving linear equality-constrained LQR problems

João Sousa-Pinto, Dominique Orban

We consider finite-horizon linear-quadratic regulator (LQR) problems with arbitrary stagewise linear equality constraints. We present a two-part reduction to an unconstrained LQR p…

math.OC2024

Primal-Dual iLQR

João Sousa-Pinto, Dominique Orban

We introduce a new algorithm for solving unconstrained discrete-time optimal control problems. Our method follows a direct multiple shooting approach, and consists of applying the…