  Within a robotic context, we merge the techniques of passivity-based control
(PBC) and reinforcement learning (RL) with the goal of eliminating some of
their reciprocal weaknesses, as well as inducing novel promising features in
the resulting framework. We frame our contribution in a scenario where PBC is
implemented by means of virtual energy tanks, a control technique developed to
achieve closed-loop passivity for any arbitrary control input. Albeit the
latter result is heavily used, we discuss why its practical application at its
current stage remains rather limited, which makes contact with the highly
debated claim that passivity-based techniques are associated with a loss of
performance. The use of RL allows us to learn a control policy that can be
passivized using the energy tank architecture, combining the versatility of
learning approaches and the system theoretic properties which can be inferred
due to the energy tanks. Simulations show the validity of the approach, as well
as novel interesting research directions in energy-aware robotics.
