Two ways of walking: Contrasting a reflexive neuro-controller and a LIP-based ZMP-controller on the humanoid robot ARMAR-4

Abstract

Full-size humanoid robots are traditionally controlled with the Zero Moment Point (ZMP)-paradigm and simplified dynamics, a well established method which can be applied to balancing, walking, and whole-body manipulation tasks. For pure walking control, approaches like pattern generators and reflexes are employed, often on optimized hardware. Both controller groups are developed on different platforms and therefore can only be indirectly compared in terms of human likeness or energy efficiency. We present a reflex based neuro-controller with an underlying, simple hill-type muscle model on the extremely versatile humanoid robot ARMAR-4. We demonstrate the reflexive controller's flexible capabilities in terms of walking speed, step length, energy efficiency and inherent robustness against fall due to small slopes and pushes along the frontal axis. We contrast this controller with a Linearized Inverted Pendulum (LIP)-based ZMP-controller on the same platform. The promising results of this study show that even general humanoid robots can benefit from reflexive control schemes and encourage further investigation in this field.