Differential Dynamic Programming for Multi-Phase Rigid Contact Dynamics

Rohan Budhiraja (1) , Justin Carpentier (1) , Carlos Mastalli (1) , Nicolas Mansard (1)

1 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes

A common strategy to generate efficient locomotion movements is to split the problem into two consecutive steps: the first one generates the contact sequence together with the centroidal trajectory, while the second step computes the whole-body trajectory that follows the centroidal pattern. While the second step is generally handled by a simple program such as an inverse kinematics solver, we propose in this paper to compute the whole-body trajectory by using a local optimal control solver, namely Differential Dynamic Programming (DDP). Our method produces more efficient motions, with lower forces and smaller impacts, by exploiting the Angular Momentum (AM). With this aim, we propose an original DDP formulation exploiting the Karush-Kuhn-Tucker constraint of the rigid contact model. We experimentally show the importance of this approach by executing large steps walking on the real HRP-2 robot, and by solving the problem of attitude control under the absence of external contact forces.

Domaines: Robotique [cs.RO]

Rohan Budhiraja, Justin Carpentier, Carlos Mastalli, Nicolas Mansard. Differential Dynamic Programming for Multi-Phase Rigid Contact Dynamics. IEEE-RAS International Conference on Humanoid Robots (Humanoids 2018), Nov 2018, Beijing, China. ⟨10.1109/HUMANOIDS.2018.8624925⟩. ⟨hal-01851596v2⟩