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    Tag: Residual Reinforcement Learning

    Sep 14
    14 min read
    Model-Predictive Control (MPC) vs. End-to-End Neural Networks: Hybrid Control Strategies

    The foundational debate in humanoid software architecture centers on a philosophical divide: first-principles physics vs. empirical data-driven learning. On one side stands the classical control community, championing Model-Predictive Control (MPC) and Quadratic Programming (QP). Rooted in classical mechanics, optimal control, and numerical optimization, MPC formulates locomotion and manipulation as explicit, constrained mathematical problems solved iteratively […]