Robotics: Science and Systems XV

Differentiable Algorithm Networks for Composable Robot Learning

Peter Karkus, Xiao Ma, David Hsu, Leslie Kaelbling, Wee Sun Lee, Tomas Lozano-Perez


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.



    AUTHOR    = {Peter Karkus AND Xiao Ma AND David Hsu AND Leslie Kaelbling AND Wee Sun Lee AND Tomas Lozano-Perez}, 
    TITLE     = {Differentiable Algorithm Networks for Composable Robot Learning}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2019}, 
    ADDRESS   = {FreiburgimBreisgau, Germany}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2019.XV.039}