Editorial: Advancements in trajectory optimization and model predictive control for legged systems

Abstract

In recent years we witnessed the proliferation of legged systems as advanced robotic platforms, which could be potentially employed in the near future on a huge spectrum of useful tasks. This is not only related to the success of quadrupeds, e.g. for inspection tasks To achieve such a level of autonomy, the use of advanced control and planning tools has been fundamental, in particular those allowing for the full exploitation of the dynamics of the system within a prediction horizon. In this respect, Trajectory Optimization (TO) and Model Predictive Control (MPC), among other methods, have proved to be highly effective on legged robotics platforms for a multitude of different tasks, including locomotion and manipulation Koenemann et al. ( While TO is commonly used to plan complex open-loop trajectories over a long prediction horizon, MPC enables fast re-planning and feedback stabilization, over a shorter horizon. Thanks to the combination of these techniques, legged systems have been able to successfully demonstrate complex high-level skills like locomanipulation in complex environments, agile locomotion, parkour, and much more in the years to come. However, it still remains an open question how to effectively employ TO and MPC on real legged systems. This requires smart formulations of contactconstrained robot dynamics, convenient models of the environment, as well as computationally efficient and real-time optimization algorithms.