  We develop a hybrid control approach for robot learning based on combining
learned predictive models with experience-based state-action policy mappings to
improve the learning capabilities of robotic systems. Predictive models provide
an understanding of the task and the physics (which improves
sample-efficiency), while experience-based policy mappings are treated as
"muscle memory" that encode favorable actions as experiences that override
planned actions. Hybrid control tools are used to create an algorithmic
approach for combining learned predictive models with experience-based
learning. Hybrid learning is presented as a method for efficiently learning
motor skills by systematically combining and improving the performance of
predictive models and experience-based policies. A deterministic variation of
hybrid learning is derived and extended into a stochastic implementation that
relaxes some of the key assumptions in the original derivation. Each variation
is tested on experience-based learning methods (where the robot interacts with
the environment to gain experience) as well as imitation learning methods
(where experience is provided through demonstrations and tested in the
environment). The results show that our method is capable of improving the
performance and sample-efficiency of learning motor skills in a variety of
experimental domains.
