  Learning from demonstrations in the wild (e.g. YouTube videos) is a
tantalizing goal in imitation learning. However, for this goal to be achieved,
imitation learning algorithms must deal with the fact that the demonstrators
and learners may have bodies that differ from one another. This condition --
"embodiment mismatch" -- is ignored by many recent imitation learning
algorithms. Our proposed imitation learning technique, SILEM (\textbf{S}keletal
feature compensation for \textbf{I}mitation \textbf{L}earning with
\textbf{E}mbodiment \textbf{M}ismatch), addresses a particular type of
embodiment mismatch by introducing a learned affine transform to compensate for
differences in the skeletal features obtained from the learner and expert. We
create toy domains based on PyBullet's HalfCheetah and Ant to assess SILEM's
benefits for this type of embodiment mismatch. We also provide qualitative and
quantitative results on more realistic problems -- teaching simulated humanoid
agents, including Atlas from Boston Dynamics, to walk by observing human
demonstrations.
