Submitted to IEEE Transactions on Robotics (T-RO)
By Armand Jordana (1), Sébastien Kleff (1), Avadesh Meduri (1), Justin Carpentier (2,3), Nicolas Mansard (4,5), Ludovic Righetti (1,5)
The promise of model-predictive control in robotics has led to extensive development of efficient numerical optimal control solvers in line with differential dynamic programming because it exploits the sparsity induced by time. In this work, we argue that this effervescence has hidden the fact that sparsity can be equally exploited by standard nonlinear optimization. In particular, we show how a tailored implementation of sequential quadratic programming achieves state-of-the-art model-predictive control. Then, we clarify the connections between popular algorithms from the robotics community and well-established optimization techniques. Further, the sequential quadratic program formulation naturally encompasses the constrained case, a notoriously difficult problem in the robotics community. Specifically, we show that it only requires a sparsity-exploiting implementation of a state-of-the-art quadratic programming solver. We illustrate the validity of this approach in a comparative study and experiments on a torque-controlled manipulator. To the best of our knowledge, this is the first demonstration of nonlinear model-predictive control with arbitrary constraints on real hardware.
Project page: https://gepettoweb.laas.fr/articles/jordana_kleff_meduri__sqp_tro_2025.html
Pre-print available at: https://hal.science/hal-04330251/
^1^ NYU - New York University [New York]^2^ NYU Tandon School of Engineering^3^ LAAS-CNRS, Université de Toulouse^4^ Inria, École normale supérieure, CNRS, PSL Research University, Paris^5^ Artificial and Natural Intelligence Toulouse Institute, Toulouse