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

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