  Physical Human-Machine Interaction plays a pivotal role in facilitating
collaboration across various domains. When designing appropriate model-based
controllers to assist a human in the interaction, the accuracy of the human
model is crucial for the resulting overall behavior of the coupled system. When
looking at state-of-the-art control approaches, most methods rely on a
deterministic model or no model at all of the human behavior. This poses a gap
to the current neuroscientific standard regarding human movement modeling,
which uses stochastic optimal control models that include signal-dependent
noise processes and therefore describe the human behavior much more accurate
than the deterministic counterparts. To close this gap by including these
stochastic human models in the control design, we introduce a novel design
methodology resulting in a Human-Variability-Respecting Optimal Control that
explicitly incorporates the human noise processes and their influence on the
mean and variability behavior of a physically coupled human-machine system. Our
approach results in an improved overall system performance, i.e. higher
accuracy and lower variability in target point reaching, while allowing to
shape the joint variability, for example to preserve human natural variability
patterns.
