Planar Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics

TL;DR

This paper presents an online nonlinear model predictive control method for bipedal robots that utilizes full-body dynamics to generate diverse, real-time walking behaviors, bridging offline gait synthesis and online reactive control.

Contribution

The work introduces a novel online NMPC approach that leverages full-order dynamics for real-time gait generation and can incorporate offline-synthesized gaits for rapid re-planning.

Findings

Successfully demonstrated on the AMBER-3M robot in simulation and hardware.

Enables diverse walking behaviors with real-time control.

Bridges offline gait synthesis with online reactive control.

Abstract

The ability to generate dynamic walking in real-time for bipedal robots with input constraints and underactuation has the potential to enable locomotion in dynamic, complex and unstructured environments. Yet, the high-dimensional nature of bipedal robots has limited the use of full-order rigid body dynamics to gaits which are synthesized offline and then tracked online. In this work we develop an online nonlinear model predictive control approach that leverages the full-order dynamics to realize diverse walking behaviors. Additionally, this approach can be coupled with gaits synthesized offline via a desired reference to enable a shorter prediction horizon and rapid online re-planning, bridging the gap between online reactive control and offline gait planning. We demonstrate the proposed method, both with and without an offline gait, on the planar robot AMBER-3M in simulation and on hardware.