# Differentiable Algorithm Networks for Composable Robot Learning

@article{Karkus2019DifferentiableAN, title={Differentiable Algorithm Networks for Composable Robot Learning}, author={Peter Karkus and Xiao Ma and David Hsu and Leslie Pack Kaelbling and Wee Sun Lee and Tomas Lozano-Perez}, journal={ArXiv}, year={2019}, volume={abs/1905.11602} }

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…

## 49 Citations

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