  We propose a learning-from-demonstration approach for grounding actions from
expert data and an algorithm for using these actions to perform a task in new
environments. Our approach is based on an application of sampling-based motion
planning to search through the tree of discrete, high-level actions constructed
from a symbolic representation of a task. Recursive sampling-based planning is
used to explore the space of possible continuous-space instantiations of these
actions. We demonstrate the utility of our approach with a magnetic structure
assembly task, showing that the robot can intelligently select a sequence of
actions in different parts of the workspace and in the presence of obstacles.
This approach can better adapt to new environments by selecting the correct
high-level actions for the particular environment while taking human
preferences into account.
