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.

通过将策略分解为独立学习的底层策略和可转移的高层策略，以简化形态的机器人为源，提出了一种层次化的策略转移方法，通过激励底层策略的学习，从而大幅提高了零样本高层策略的可转移性。同时，采用KL正则化训练高层策略会稳定学习并防止模式崩溃，进一步在一系列公共环境中验证了该方法的适用性。

分层解耦模仿用于形态转移