  In this letter, we present an approach for learning in-hand manipulation
skills with a low-cost, underactuated prosthetic hand in the presence of
irreversible events. Our approach combines reinforcement learning based on
visual perception with low-level reactive control based on tactile perception,
which aims to avoid slipping. The objective of the reinforcement learning level
consists not only in fulfilling the in-hand manipulation goal, but also in
minimizing the intervention of the tactile reactive control. This way, the
occurrence of object slipping during the learning procedure, which we consider
an irreversible event, is significantly reduced. When an irreversible event
occurs, the learning process is considered failed. We show the performance in
two tasks, which consist in reorienting a cup and a bottle only using the
fingers. The experimental results show that the proposed architecture allows
reaching the goal in the Cartesian space and reduces significantly the
occurrence of object slipping during the learning procedure. Moreover, without
the proposed synergy between reactive control and reinforcement learning it was
not possible to avoid irreversible events and, therefore, to learn the task.
