  Some Learning from Demonstrations (LfD) methods handle small mismatches in
the action spaces of the teacher and student. Here we address the case where
the teacher's morphology is substantially different from that of the student.
Our framework, Morphological Adaptation in Imitation Learning (MAIL), bridges
this gap allowing us to train an agent from demonstrations by other agents with
significantly different morphologies. MAIL learns from suboptimal
demonstrations, so long as they provide $\textit{some}$ guidance towards a
desired solution. We demonstrate MAIL on manipulation tasks with rigid and
deformable objects including 3D cloth manipulation interacting with rigid
obstacles. We train a visual control policy for a robot with one end-effector
using demonstrations from a simulated agent with two end-effectors. MAIL shows
up to $24\%$ improvement in a normalized performance metric over LfD and
non-LfD baselines. It is deployed to a real Franka Panda robot, handles
multiple variations in properties for objects (size, rotation, translation),
and cloth-specific properties (color, thickness, size, material). An overview
is on https://uscresl.github.io/mail .
