Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control
This work addresses the need for efficient and versatile optimal control in robotics, particularly for multi-contact scenarios, though it appears incremental as it builds on classical DDP methods.
The authors tackled the problem of efficiently computing state trajectories and control policies for multi-contact optimal control in robotics, introducing the Crocoddyl framework and a novel Feasibility-driven Differential Dynamic Programming (FDDP) algorithm that enables computation of highly-dynamic maneuvers like jumping and front-flips within milliseconds.
We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently computes the state trajectory and the control policy for a given predefined sequence of contacts. Its efficiency is due to the use of sparse analytical derivatives, exploitation of the problem structure, and data sharing. It employs differential geometry to properly describe the state of any geometrical system, e.g. floating-base systems. Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP). Our method does not add extra decision variables which often increases the computation time per iteration due to factorization. FDDP shows a greater globalization strategy compared to classical Differential Dynamic Programming (DDP) algorithms. Concretely, we propose two modifications to the classical DDP algorithm. First, the backward pass accepts infeasible state-control trajectories. Second, the rollout keeps the gaps open during the early "exploratory" iterations (as expected in multiple-shooting methods with only equality constraints). We showcase the performance of our framework using different tasks. With our method, we can compute highly-dynamic maneuvers (e.g. jumping, front-flip) within few milliseconds.