Asynchronous Real-Time Optimization of Footstep Placement and Timing in Bipedal Walking Robots

TL;DR

This paper presents ARTO, a novel asynchronous optimization method for real-time bipedal robot walking that optimizes footstep placement and timing by combining fast gradient descent with accurate dynamics approximation.

Contribution

The paper introduces ARTO, an innovative asynchronous optimization framework that simultaneously leverages high-frequency gradient descent and lower-frequency dynamics-based optimization for improved footstep planning.

Findings

ARTO recovers from larger pushes than standard methods.

ARTO produces feasible solutions for larger velocity changes.

ARTO outperforms single optimizer approaches in experiments.

Abstract

Online footstep planning is essential for bipedal walking robots to be able to walk in the presence of disturbances. Until recently this has been achieved by only optimizing the placement of the footstep, keeping the duration of the step constant. In this paper we introduce a footstep planner capable of optimizing footstep placement and timing in real-time by asynchronously combining two optimizers, which we refer to as asynchronous real-time optimization (ARTO). The first optimizer which runs at approximately 25 Hz, utilizes a fourth-order Runge-Kutta (RK4) method to accurately approximate the dynamics of the linear inverted pendulum (LIP) model for bipedal walking, then uses non-linear optimization to find optimal footsteps and duration at a lower frequency. The second optimizer that runs at approximately 250 Hz, uses analytical gradients derived from the full dynamics of the LIP model and constraint penalty terms to perform gradient descent, which finds approximately optimal footstep placement and timing at a higher frequency. By combining the two optimizers asynchronously, ARTO has the benefits of fast reactions to disturbances from the gradient descent optimizer, accurate solutions that avoid local optima from the RK4 optimizer, and increases the probability that a feasible solution will be found from the two optimizers. Experimentally, we show that ARTO is able to recover from considerably larger pushes and produces feasible solutions to larger reference velocity changes than a standard footstep location optimizer, and outperforms using just the RK4 optimizer alone.