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The limits and potentials of deep learning for robotics

Year: 2018
Type: article
Abstract: 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.
DOI: 10.1177/0278364918770733
Authors: Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, Peter Corke
Institutions: Queensland University of Technology, Australian Centre for Robotic Vision, Technische Universität Berlin, University of Notre Dame, Google DeepMind (United Kingdom), University of Washington, University of California, Berkeley, University of Freiburg
Language: English
Open Access status: green
OpenAlex ID: W2797862905
FWCI: 30.25
Cites: 126
Cited by: 527
Related to: 10