Semantic Scholar Open Access 2018 558 sitasi

The limits and potentials of deep learning for robotics

Niko Sünderhauf O. Brock W. Scheirer R. Hadsell D. Fox +6 lainnya

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Abstrak

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.

Topik & Kata Kunci

Mathematics Computer Science Engineering

Penulis (11)

Niko Sünderhauf

O. Brock

W. Scheirer

R. Hadsell

D. Fox

J. Leitner

B. Upcroft

P. Abbeel

Wolfram Burgard

Michael Milford

Peter Corke

Format Sitasi

APA MLA BibTeX

Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J. et al. (2018). The limits and potentials of deep learning for robotics. https://doi.org/10.1177/0278364918770733

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Lihat di Sumber doi.org/10.1177/0278364918770733

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 558×
Sumber Database: Semantic Scholar
DOI: 10.1177/0278364918770733
Akses: Open Access ✓