The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
A Survey of Research on Cloud Robotics and Automation
Open accessAbstract
The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/.
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Cites
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Large Scale Distributed Deep Networks
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Cited by
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364917710318 · Cited by 1,971
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
Mobile Edge Intelligence and Computing for the Internet of Vehicles
Zhang, J., & Letaief, K. B. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2947490 · Cited by 537
Artificial intelligence: Implications for the future of work
Howard, J. (2019) · American Journal of Industrial Medicine · doi:10.1002/ajim.23037 · Cited by 522
Metadata
- Authors
- Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K.
- Venue
- IEEE Transactions on Automation Science and Engineering
- Published
- 2015-04
- Volume
- 12
- Issue
- 2
- Pages
- 398-409
- ISSN
- 1545-5955 (ISSN-L)
- 1558-3783
- OpenAlex
- W2085302753
- Cited by
- 833
- References
- 95
- FWCI
- 83.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015). A Survey of Research on Cloud Robotics and Automation. IEEE Transactions on Automation Science and Engineering. https://doi.org/10.1109/tase.2014.2376492
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
