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Robot mistakes are surprisingly forgiving – humans penalize slips and freezes more harshly, and sometimes even interpret certain mistakes as successes.
Achieve faster and more reliable robot motion planning by reactively switching between simplified models within a Nonlinear Model Predictive Control framework, guided by deep reinforcement learning.
