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Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a
composable architecture for robot learning systems. A DAN is composed of neural
network modules, each encoding a differentiable robot algorithm and an
associated model; and it is trained end-to-end from data. DAN combines the
strengths of model-driven modular system design and data-driven end-to-end
learning. The algorithms and models act as structural assumptions to reduce the
data requirements for learning; end-to-end learning allows the modules to adapt
to one another and compensate for imperfect models and algorithms, in order to
achieve the best overall system performance. We illustrate the DAN methodology
through a case study on a simulated robot system, which learns to navigate in
complex 3-D environments with only local visual observations and an image of a
partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at
https://youtu.be/4jcYlTSJF4
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