In this paper we present a hybrid control scheme, combining the advantages of task-space
and joint-space control. The controller is based on a human-like adaptive design, which minimises
both control effort and tracking error. Our novel hybrid adaptive controller has been
tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected
by external disturbances in the form of interaction with the environment and tool-like end-effector
perturbations. The results demonstrated improved performance in the hybrid controller
over both of its component parts. In addition, we introduce a novel method for online
adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge
from the experimenter. This mechanism of meta-learning induces further improvement
in performance and avoids the need for tuning through trial testing.