Optimal Reduced-order Modeling of Bipedal Locomotion

TL;DR

This paper presents a method to automatically synthesize low-dimensional models for bipedal locomotion that retain high-dimensional system capabilities, improving task complexity and agility.

Contribution

It introduces an optimization algorithm for designing reduced-order models that balance simplicity and performance in legged locomotion.

Findings

Optimized models for walking at various speeds and inclines.

Successful application to a five-link model and the Cassie robot.

Enhanced model capabilities while maintaining low complexity.

Abstract

State-of-the-art approaches to legged locomotion are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), popular because their simplicity enables a wide array of tools for planning, control, and analysis. However, they inevitably limit the ability to execute complex tasks or agile maneuvers. In this work, we aim to automatically synthesize models that remain low-dimensional but retain the capabilities of the high-dimensional system. For example, if one were to restore a small degree of complexity to LIP, SLIP, or a similar model, our approach discovers the form of that additional complexity which optimizes performance. In this paper, we define a class of reduced-order models and provide an algorithm for optimization within this class. To demonstrate our method, we optimize models for walking at a range of speeds and ground inclines, for both a five-link model and the Cassie bipedal robot.