  Learning long-range behaviors on complex high-dimensional agents is a
fundamental problem in robot learning. For such tasks, we argue that
transferring learned information from a morphologically simpler agent can
massively improve the sample efficiency of a more complex one. To this end, we
propose a hierarchical decoupling of policies into two parts: an independently
learned low-level policy and a transferable high-level policy. To remedy poor
transfer performance due to mismatch in morphologies, we contribute two key
ideas. First, we show that incentivizing a complex agent's low-level to imitate
a simpler agent's low-level significantly improves zero-shot high-level
transfer. Second, we show that KL-regularized training of the high level
stabilizes learning and prevents mode-collapse. Finally, on a suite of publicly
released navigation and manipulation environments, we demonstrate the
applicability of hierarchical transfer on long-range tasks across morphologies.
Our code and videos can be found at
https://sites.google.com/berkeley.edu/morphology-transfer.
