This paper presents a methodology for translating a key feature encoded in human locomotion — multi-contact behavior — to a physical 2D bipedal robot, AMBER2, by leveraging novel controller design, optimization methods, and software structures for the translation to hardware. This paper begins with the analysis of human locomotion data and uses it to motivate the construction of a hybrid system model representing a multi-contact robotic walking gait. By again looking to human data for inspiration, human-inspired controllers are developed and used in the formulation of an optimization problem that yields stable human-like multi-domain walking in simulation. These formal results are translated to hardware implementation via a novel dynamic trajectory generation strategy. Finally, the specific software structures utilized to translate these trajectories to hardware are presented. The end result is experimentally realized stable robotic walking with remarkably human-like multi-contact foot behaviors.
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