Prioritized Optimal Control: a Hierarchical Differential Dynamic Programming approach

Francesco Romano (1) , Andrea del Prete (2) , Nicolas Mansard (2) , Francesco Nori (1)

1 IIT - Istituto Italiano di Tecnologia
2 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes

This paper deals with the generation of motion for complex dynamical systems (such as humanoid robots) to achieve several concurrent objectives. Hierarchy of tasks and optimal control are two frameworks commonly used to this aim. The first one specifies control objectives as a number of quadratic functions to be minimized under strict priorities. The second one minimizes an arbitrary user-defined function of the future state of the system, thus considering its evolution in time. Our recent work on prioritized optimal control merges the advantages of both these methods. This paper reformulates the original prioritized optimal control algorithm with the precise goal of improving its computational speed. We extend the dynamic programming method to work with a hierarchy of tasks. We compared our approach in simulation with both our previous algorithm and classical optimal control. The measured computational improvement represents another step towards the application of prioritized optimal control for online model predictive control of humanoid robots. We believe that this could be the key to unlock the (so far unexploited) dynamic capabilities of these mechanical systems.

Mots clés: Optimal Control; Dynamic Programming; Humanoid Whole-Body Control; Prioritized control

Domaines: Robotique [cs.RO]

Francesco Romano, Andrea del Prete, Nicolas Mansard, Francesco Nori. Prioritized Optimal Control: a Hierarchical Differential Dynamic Programming approach. 2015 IEEE International Conference on Robotics and Automation (ICRA 2015), May 2015, Washington State Convention Center, Seattle, Washington, United States. ⟨hal-01121341⟩