  Imitation learning enables robots to learn from demonstrations. Previous
imitation learning algorithms usually assume access to optimal expert
demonstrations. However, in many real-world applications, this assumption is
limiting. Most collected demonstrations are not optimal or are produced by an
agent with slightly different dynamics. We therefore address the problem of
imitation learning when the demonstrations can be sub-optimal or be drawn from
agents with varying dynamics. We develop a metric composed of a feasibility
score and an optimality score to measure how useful a demonstration is for
imitation learning. The proposed score enables learning from more informative
demonstrations, and disregarding the less relevant demonstrations. Our
experiments on four environments in simulation and on a real robot show
improved learned policies with higher expected return.
