Toward an Expressive Bipedal Robot: Variable Gait Synthesis and Validation in a Planar Model

TL;DR

This paper introduces a framework for generating a wide variety of expressive bipedal walking gaits in a planar robot model, inspired by human motion, validated through user studies to ensure distinguishability and meaningfulness.

Contribution

It presents a novel model-based trajectory optimization method for creating diverse, stylistic gaits with validated human-like expressiveness in a planar biped model.

Findings

Generated 360 distinct gaits with variable styles.

Validated that users can distinguish and interpret gait styles.

Demonstrated potential for socially expressive humanoid robots.

Abstract

Humans are efficient, yet expressive in their motion. Human walking behaviors can be used to walk across a great variety of surfaces without falling and to communicate internal state to other humans through variable gait styles. This provides inspiration for creating similarly expressive bipedal robots. To this end, a framework is presented for stylistic gait generation in a compass-like under-actuated planar biped model. The gait design is done using model-based trajectory optimization with variable constraints. For a finite range of optimization parameters, a large set of 360 gaits can be generated for this model. In particular, step length and cost function are varied to produce distinct cyclic walking gaits. From these resulting gaits, 6 gaits are identified and labeled, using embodied movement analysis, with stylistic verbs that correlate with human activity, e.g., "lope" and "saunter". These labels have been validated by conducting user studies in Amazon Mechanical Turk and thus demonstrate that visually distinguishable, meaningful gaits are generated using this framework. This lays groundwork for creating a bipedal humanoid with variable socially competent movement profiles.