  Force control is essential for medical robots when touching and contacting
the patient's body. To increase the stability and efficiency in force control,
an Adaption Module could be used to adjust the parameters for different contact
situations. We propose an adaptive controller with an Adaption Module which can
produce control parameters based on force feedback and real-time stiffness
detection. We develop methods for learning the optimal policies by value
iteration and using the data generated from those policies to train the
Adaptive Module. We test this controller on different zones of a person's arm.
All the parameters used in practice are learned from data. The experiments show
that the proposed adaptive controller can exert various target forces on
different zones of the arm with fast convergence and good stability.
