The limits and potentials of deep learning for robotics
Suenderhauf, Niko, Brock, Oliver, Scheirer, Walter, Hadsell, Raia, Fox, Dieter, Leitner, Jurgen, Upcroft, Ben, Abbeel, Pieter, Burgard, Wolfram, Milford, Michael, & Corke, Peter (2018) The limits and potentials of deep learning for robotics. International Journal of Robotics Research, 37(4 - 5), pp. 405-420.
|
Submitted Version
(PDF 490kB)
1804.06557.pdf. |
Description
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.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 121238 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Item Type: | Contribution to Journal (Journal Article) | ||||||||
| Refereed: | Yes | ||||||||
| ORCID iD: |
|
||||||||
| Measurements or Duration: | 16 pages | ||||||||
| Keywords: | artificial intelligence, deep learning, robotics | ||||||||
| DOI: | 10.1177/0278364918770733 | ||||||||
| ISSN: | 1741-3176 | ||||||||
| Pure ID: | 33369706 | ||||||||
| Divisions: | Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Research Centres > ARC Centre of Excellence for Robotic Vision |
||||||||
| Funding: | |||||||||
| Copyright Owner: | Consult author(s) regarding copyright matters | ||||||||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||||
| Deposited On: | 12 Sep 2018 13:56 | ||||||||
| Last Modified: | 07 Jun 2026 18:28 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page