The limits and potentials of deep learning for robotics

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep...

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Bibliographic Details
Published in:The International journal of robotics research 2018-04, Vol.37 (4-5), p.405-420
Main Authors: Sünderhauf, Niko, Brock, Oliver, Scheirer, Walter, Hadsell, Raia, Fox, Dieter, Leitner, Jürgen, Upcroft, Ben, Abbeel, Pieter, Burgard, Wolfram, Milford, Michael, Corke, Peter
Format: Article
Language:English
Subjects:
Computer simulation
Computer vision
Deep learning
Machine learning
Robotics
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Summary:The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364918770733