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Poster
Hierarchically Decoupled Imitation For Morphological Transfer
Donald Hejna · Lerrel Pinto · Pieter Abbeel

Tue Jul 14 09:00 AM -- 09:45 AM & Tue Jul 14 08:00 PM -- 08:45 PM (PDT) @ None #None

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.

Author Information

Donald Hejna (UC Berkeley)
Lerrel Pinto (NYU/Berkeley)
Pieter Abbeel (UC Berkeley)

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