  The goal of learning from demonstrations is to learn a policy for an agent
(imitator) by mimicking the behavior in the demonstrations. Prior works on
learning from demonstrations assume that the demonstrations are collected by a
demonstrator that has the same dynamics as the imitator. However, in many
real-world applications, this assumption is limiting -- to improve the problem
of lack of data in robotics, we would like to be able to leverage
demonstrations collected from agents with different dynamics. This can be
challenging as the demonstrations might not even be feasible for the imitator.
Our insight is that we can learn a feasibility metric that captures the
likelihood of a demonstration being feasible by the imitator. We develop a
feasibility MDP (f-MDP) and derive the feasibility score by learning an optimal
policy in the f-MDP. Our proposed feasibility measure encourages the imitator
to learn from more informative demonstrations, and disregard the far from
feasible demonstrations. Our experiments on four simulated environments and on
a real robot show that the policy learned with our approach achieves a higher
expected return than prior works. We show the videos of the real robot arm
experiments on our website
(https://sites.google.com/view/learning-feasibility).
