Staged Contact Optimization: Combining Contact-Implicit and Multi-Phase Hybrid Trajectory Optimization

TL;DR

This paper introduces Staged Contact Optimization (SCO), a hybrid approach that combines contact-implicit and multi-phase hybrid trajectory optimization to improve convergence speed and solution quality for legged robot trajectories.

Contribution

The paper presents SCO, a novel algorithm that stages contact constraints to enhance robustness and efficiency in trajectory optimization for legged robots.

Findings

SCO outperforms CIO and HTO in speed and optimality.

SCO demonstrates effective trajectory generation for bipeds and quadrupeds.

The method is suitable for offline planning and dynamic capability analysis.

Abstract

Trajectory optimization problems for legged robots are commonly formulated with fixed contact schedules. These multi-phase Hybrid Trajectory Optimization (HTO) methods result in locally optimal trajectories, but the result depends heavily upon the predefined contact mode sequence. Contact-Implicit Optimization (CIO) offers a potential solution to this issue by allowing the contact mode to be determined throughout the trajectory by the optimization solver. However, CIO suffers from long solve times and convergence issues. This work combines the benefits of these two methods into one algorithm: Staged Contact Optimization (SCO). SCO tightens constraints on contact in stages, eventually fixing them to allow robust and fast convergence to a feasible solution. Results on a planar biped and spatial quadruped demonstrate speed and optimality improvements over CIO and HTO. These properties make SCO well suited for offline trajectory generation or as an effective tool for exploring the dynamic capabilities of a robot.