A human-inspired framework for bipedal robotic walking design

Abstract

This work seeks virtual constraints, or outputs, that are intrinsic to human walking and utilises these outputs to construct controllers which produce human-like bipedal robotic walking. Beginning with experimental human walking data, human outputs are sought, i.e., functions of the kinematics of the human over time, which provides a low-dimensional representation of human walking. It will be shown that, for these outputs, humans act like linear mass-spring-dampers; this yields a time representation of the human outputs through canonical walking functions. Combining these formulations leads to human-inspired controllers that, when utilised in an optimisation problem, provably result in robotic walking that is as ‘human-like’ as possible. This human-inspired approach is applied to multiple human output combinations, from which it is determined which output combination results in the most human-like walking for a robotic model with mean human parameters.