Regularized Hierarchical Differential Dynamic Programming

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

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

This paper presents a new algorithm for optimal control (OC) of nonlinear dynamical systems. The main feature of this algorithm is that it allows the specification of the control objectives as a hierarchy of tasks. Each task is described by a cost function that the algorithm tries to minimize, while not affecting the tasks of higher priority. The concept of strict priority allows for an easier and more robust specification of the control objectives, without hand-tuning of task weights. The hierarchy also makes it possible to properly regularize the behavior of each task independently. For the first time, we properly define the problem of regularizing the task cost functions in the presence of a hierarchy and propose an algorithm to compute an approximate solution. Several simulated scenarios with different robots compare our solution with other state-of-the-art methods, validating the interest of the hierarchy in OC and empirically demonstrating the importance of regularization to generate safe behaviors.

Domaines: Robotique [cs.RO]

Mathieu Geisert, Andrea del Prete, Nicolas Mansard, Francesco Romano, Francesco Nori. Regularized Hierarchical Differential Dynamic Programming. IEEE Transactions on Robotics, 2017, 33 (4), pp.819-833. ⟨hal-01356992v2⟩