Differentiable Algorithm Networks for Composable Robot Learning
| Autor: | Peter Karkus, Xiao Ma, David Hsu, Leslie Kaelbling, Wee Sun Lee, Tomas Lozano-Perez |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning Computer Science - Artificial Intelligence Machine Learning (stat.ML) 02 engineering and technology Machine Learning (cs.LG) Computer Science - Robotics Artificial Intelligence (cs.AI) 020901 industrial engineering & automation Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Robotics (cs.RO) |
| Zdroj: | arXiv |
| Popis: | 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. RSS 2019 camera ready. Video is available at https://youtu.be/4jcYlTSJF4Y |
| Databáze: | OpenAIRE |
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