First, if optimal control is hard, how do humans accurately solve day to day control problems? In the first part of the talk, I propose that for stereotypical movements like reaching, the optimal control solution can be decomposed as a sum of scaled and time shifted components, or ``motor synergies''. These synergies were discovered through dimensionality reduction. Near-optimal control is achieved by linearly combining the synergies. Second, are humans optimal under non-stereotypical conditions? In the second part of the talk, I present results from experiments that show that humans are able to perform well even under certain non-stereotypical control regimes. In these experiments, subjects were required to control a dynamical system corrupted with noise. A comparison of human performance to the theoretically optimal solution shows that humans reach close to optimal performance under a variety of noise conditions. Additionally, we showed that subjects adapt to the noise regime rather than using a fixed controller across different noise conditions.