  Assistive Exoskeleton Robots are helping restore functions to people
suffering from underlying medical conditions. These robots require precise
tuning of hyper-parameters to feel natural to the user. The device
hyper-parameters often need to be re-tuned from task to task, which can be
tedious and require expert knowledge. To address this issue, we develop a set
of fuzzy logic controllers that can dynamically tune robot gain parameters to
adapt its sensitivity to the user's intention determined from muscle
activation. The designed fuzzy controllers benefit from a set of expert-defined
rules and do not rely on extensive amounts of training data. We evaluate the
designed controllers with three different tasks and compare our results against
the manually tuned system. Our preliminary results show that our controllers
reduce the amount of fighting between the device and the human, measured using
a set of pressure sensors.
