  The underwater environment poses a complex problem for developing autonomous
capabilities for Underwater Vehicle Manipulator Systems (UVMSs). The modeling
of UVMSs is a complicated and costly process due to the highly nonlinear
dynamics and the presence of unknown hydrodynamical effects. This is aggravated
in tasks where the manipulation of objects is necessary, as this may not only
introduce external disturbances that can lead to a fast degradation of the
control system performance, but also requires the coordinating with a vision
system for the correct grasping and operation of the object. In this article,
we introduce a control strategy for UVMSs working with unknown payloads. The
proposed control strategy is based on a data-driven optimal controller. We
present a number of experimental results showing the benefits of the proposed
strategy. Furthermore, we include a discussion regarding the visual perception
requirements for the UVMS in order to achieve full autonomy in underwater
manipulation tasks of unknown payloads.
