What is the impact of unsupervised latent action discovery on cross-domain transfer performance in continuous control tasks

Abstract

Abstract: The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot (sim-to-real). Despite becoming increasingly realistic, all simulators are by construction based on models, hence inevitably imperfect. This raises the question of how simulators can be modified to facilit

Research Question

What is the impact of unsupervised latent action discovery on cross-domain transfer performance in continuous control tasks evaluated on standard robotic suites?

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