  Compliant robotics have seen successful applications in energy efficient
locomotion and cyclic manipulation. However, exploitation of variable physical
impedance for energy efficient sequential movements has not been extensively
addressed. This work employs a hierarchical approach to encapsulate low-level
optimal control for sub-movement generation into an outer loop of iterative
policy improvement, thereby leveraging the benefits of both optimal control and
reinforcement learning. The framework enables optimizing efficiency trade-off
for minimal energy expenses in a model-free manner, by taking account of cost
function weighting, variable impedance exploitation, and transition timing --
which are associated with the skill of compliance. The effectiveness of the
proposed method is evaluated using two consecutive reaching tasks on a variable
impedance actuator. The results demonstrate significant energy saving by
improving the skill of compliance, with an electrical consumption reduction of
about 30% measured in a physical robot experiment.
