  In this work, we explore a novel framework for control of complex systems
called Primitive Imitation for Control PICO. The approach combines ideas from
imitation learning, task decomposition, and novel task sequencing to generalize
from demonstrations to new behaviors. Demonstrations are automatically
decomposed into existing or missing sub-behaviors which allows the framework to
identify novel behaviors while not duplicating existing behaviors.
Generalization to new tasks is achieved through dynamic blending of behavior
primitives. We evaluated the approach using demonstrations from two different
robotic platforms. The experimental results show that PICO is able to detect
the presence of a novel behavior primitive and build the missing control
policy.
