  Robots are often built from standardized assemblies, (e.g. arms, legs, or
fingers), but each robot must be trained from scratch to control all the
actuators of all the parts together. In this paper we demonstrate a new
approach that takes a single robot and its controller as input and produces a
set of modular controllers for each of these assemblies such that when a new
robot is built from the same parts, its control can be quickly learned by
reusing the modular controllers. We achieve this with a framework called MeMo
which learns (Me)aningful, (Mo)dular controllers. Specifically, we propose a
novel modularity objective to learn an appropriate division of labor among the
modules. We demonstrate that this objective can be optimized simultaneously
with standard behavior cloning loss via noise injection. We benchmark our
framework in locomotion and grasping environments on simple to complex robot
morphology transfer. We also show that the modules help in task transfer. On
both structure and task transfer, MeMo achieves improved training efficiency to
graph neural network and Transformer baselines.
