RI: Medium: Combining Optimal and Neuromuscular Controllers for Agile and Robust Humanoid Behavior

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The goal of making humanoid robots that assist people in the real world a reality requires that these robots can walk reliably in many different environments and terrains. This project leverages strong progress made by the PIs in the DARPA Robotics Challenge to address new techniques for whole-body locomotion of humanoid robots, based on the blending of model-based and parametric control approaches.A central conundrum in humanoid robotics is that manually designed, policy-based controllers are often more robust than model-based optimizing controllers, but model-based controllers more easily generalize to automated behavior generation with direct control of foot or hand placements. This project tests the hypothesis that incorporating policy-based control as either a cost, a constraint, or a replacement within the model-based online optimal control framework produces controllers that combine the advantages of both approaches.