  We describe an algorithm for motion planning based on expert demonstrations
of a skill. In order to teach robots to perform complex object manipulation
tasks that can generalize robustly to new environments, we must (1) learn a
representation of the effects of a task and (2) find an optimal trajectory that
will reproduce these effects in a new environment. We represent robot skills in
terms of a probability distribution over features learned from multiple expert
demonstrations. When utilizing a skill in a new environment, we compute feature
expectations over trajectory samples in order to stochastically optimize the
likelihood of a trajectory in the new environment. The purpose of this method
is to enable execution of complex tasks based on a library of probabilistic
skill models. Motions can be combined to accomplish complex tasks in hybrid
domains. Our approach is validated in a variety of case studies, including an
Android game, simulated assembly task, and real robot experiment with a UR5.
