  This paper explores the idea that skillful assembly is best represented as
dynamic sequences of Manipulation Primitives, and that such sequences can be
automatically discovered by Reinforcement Learning. Manipulation Primitives,
such as "Move down until contact", "Slide along x while maintaining contact
with the surface", have enough complexity to keep the search tree shallow, yet
are generic enough to generalize across a wide range of assembly tasks.
Moreover, the additional "semantics" of the Manipulation Primitives make them
more robust in sim2real and against model/environment variations and
uncertainties, as compared to more elementary actions. Policies are learned in
simulation, and then transferred onto a physical platform. Direct sim2real
transfer (without retraining in real) achieves excellent success rates on
challenging assembly tasks, such as round peg insertion with 0.04 mm clearance
or square peg insertion with large hole position/orientation estimation errors.
