  Differential dynamic programming (DDP) is a direct single shooting method for
trajectory optimization. Its efficiency derives from the exploitation of
temporal structure (inherent to optimal control problems) and explicit
roll-out/integration of the system dynamics. However, it suffers from numerical
instability and, when compared to direct multiple shooting methods, it has
limited initialization options (allows initialization of controls, but not of
states) and lacks proper handling of control constraints. In this work, we
tackle these issues with a feasibility-driven approach that regulates the
dynamic feasibility during the numerical optimization and ensures control
limits. Our feasibility search emulates the numerical resolution of a direct
multiple shooting problem with only dynamics constraints. We show that our
approach (named BOX-FDDP) has better numerical convergence than BOX-DDP+ (a
single shooting method), and that its convergence rate and runtime performance
are competitive with state-of-the-art direct transcription formulations solved
using the interior point and active set algorithms available in KNITRO. We
further show that BOX-FDDP decreases the dynamic feasibility error
monotonically--as in state-of-the-art nonlinear programming algorithms. We
demonstrate the benefits of our approach by generating complex and athletic
motions for quadruped and humanoid robots. Finally, we highlight that BOX-FDDP
is suitable for model predictive control in legged robots.

Robotics;Artificial Intelligence;Systems and Control;Systems and Control