Model predictive control of legged and humanoid robots: models and algorithms

Model predictive control (MPC) of legged and humanoid robotic systems has been an active research topic in the past decade. While MPC for robotic systems has a long history, its paradigm such as problem formulations and algorithms has changed along with the recent drastic progress in robot hardware, computational processors, and algorithms. This survey paper reviews recent progress on MPC for legged and humanoid robotics from the following three points of view. First, we review a variety of dynamical models of robotic systems used in the MPC formulation. Second, we give an overview of MPC algorithms, particularly focusing on suitable ones for robotic problems. Finally, we introduce methods and applications of MPC for practical robotic problems from MPC based on reduced-order models to recent progress on MPC based on whole-body models.