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We propose a method to generate actuation plans for a reduced order, dynamicmodel of bipedal running. This method explicitly enforces robustness to grounduncertainty. The plan generated is not a fixed body trajectory that isaggressively stabilized: instead, the plan interacts with the passive dynamicsof the reduced order model to create emergent robustness. The goal is to createplans for legged robots that will be robust to imperfect perception of theenvironment, and to work with dynamics that are too complex to optimize inreal-time. Working within this dynamic model of legged locomotion, we optimizea set of disturbance cases together with the nominal case, all with linkedinputs. The input linking is nontrivial due to the hybrid dynamics of therunning model but our solution is effective and has analytical gradients. Theoptimization procedure proposed is significantly slower than a standardtrajectory optimization, but results in robust gaits that reject disturbancesextremely effectively without any replanning required.

Planning for the Unexpected: Explicitly Optimizing Motions for Ground Uncertainty in Running