  The objective of this work is to augment the basic abilities of a robot by
learning to use sensorimotor primitives to solve complex long-horizon
manipulation problems. This requires flexible generative planning that can
combine primitive abilities in novel combinations and thus generalize across a
wide variety of problems. In order to plan with primitive actions, we must have
models of the actions: under what circumstances will executing this primitive
successfully achieve some particular effect in the world?
  We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
constraints on skill effectiveness from small numbers of expensive-to-collect
training examples. Additionally, we develop efficient adaptive sampling methods
for generating a comprehensive and diverse sequence of continuous candidate
control parameter values (such as pouring waypoints for a cup) during planning.
These values become end-effector goals for traditional motion planners that
then solve for a full robot motion that performs the skill. By using learning
and planning methods in conjunction, we take advantage of the strengths of each
and plan for a wide variety of complex dynamic manipulation tasks. We
demonstrate our approach in an integrated system, combining traditional
robotics primitives with our newly learned models using an efficient robot task
and motion planner. We evaluate our approach both in simulation and in the real
world through measuring the quality of the selected primitive actions. Finally,
we apply our integrated system to a variety of long-horizon simulated and
real-world manipulation problems.
