Multi-Shooting Differential Dynamic Programming for Hybrid Systems using Analytical Derivatives

TL;DR

This paper advances trajectory optimization for hybrid robotic systems by developing second-order analytical derivatives and a Quasi-Newton approximation, significantly improving convergence speed over traditional methods.

Contribution

It introduces second-order analytical derivatives for contact dynamics and a Quasi-Newton approach, enhancing the efficiency of Multi-Shooting DDP for complex robotic motions.

Findings

Second-order derivatives improve convergence speed.

Quasi-Newton method achieves order-of-magnitude speedups.

Enhanced trajectory optimization for quadruped gaits.

Abstract

Differential Dynamic Programming (DDP) is a popular technique used to generate motion for dynamic-legged robots in the recent past. However, in most cases, only the first-order partial derivatives of the underlying dynamics are used, resulting in the iLQR approach. Neglecting the second-order terms often slows down the convergence rate compared to full DDP. Multi-Shooting is another popular technique to improve robustness, especially if the dynamics are highly non-linear. In this work, we consider Multi-Shooting DDP for trajectory optimization of a bounding gait for a simplified quadruped model. As the main contribution, we develop Second-Order analytical partial derivatives of the rigid-body contact dynamics, extending our previous results for fixed/floating base models with multi-DoF joints. Finally, we show the benefits of a novel Quasi-Newton method for approximating second-order derivatives of the dynamics, leading to order-of-magnitude speedups in the convergence compared to the full DDP method.