Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

TL;DR

Crocoddyl is an open-source framework for efficient multi-contact optimal control, utilizing sparse derivatives and a novel FDDP algorithm to enable fast computation of complex robotic maneuvers.

Contribution

The paper introduces Crocoddyl and a new FDDP algorithm that improves multi-contact optimal control efficiency without increasing decision variables.

Findings

Enables computation of dynamic maneuvers within milliseconds

Uses differential geometry for accurate state representation

Demonstrates effectiveness on tasks like jumping and flipping

Abstract

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.