Hierarchically Decoupled Imitation for Morphological Transfer

• URL: http://arxiv.org/abs/2003.01709v2
• Date: Mon, 31 Aug 2020 07:26:59 GMT
• Title: Hierarchically Decoupled Imitation for Morphological Transfer
• Authors: Donald J. Hejna III, Pieter Abbeel, Lerrel Pinto
• Abstract summary: We show that transferring learned information from a morphologically simpler agent can massively improve the sample efficiency of a more complex one. 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.
• Score: 95.19299356298876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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