From Human Walking to Bipedal Robot Locomotion: Reflex Inspired Compensation on Planned and Unplanned Downsteps

TL;DR

This paper investigates how human-like responses to planned and unplanned downsteps can be translated into bipedal robot locomotion using reduced-order modeling and nonlinear optimization, demonstrating successful simulation results.

Contribution

It introduces a systematic method to transfer human downstep behaviors to robots through reduced-order models and optimization-based control, addressing morphology differences.

Findings

Successful simulation of planned downsteps on a bipedal robot.

Effective translation of human CoM and contact force data into robot control.

Demonstration of unplanned downstep handling in simulation.

Abstract

Humans are able to negotiate downstep behaviors -- both planned and unplanned -- with remarkable agility and ease. The goal of this paper is to systematically study the translation of this human behavior to bipedal walking robots, even if the morphology is inherently different. Concretely, we begin with human data wherein planned and unplanned downsteps are taken. We analyze this data from the perspective of reduced-order modeling of the human, encoding the center of mass (CoM) kinematics and contact forces, which allows for the translation of these behaviors into the corresponding reduced-order model of a bipedal robot. We embed the resulting behaviors into the full-order dynamics of a bipedal robot via nonlinear optimization-based controllers. The end result is the demonstration of planned and unplanned downsteps in simulation on an underactuated walking robot.