A Survey of Research on Cloud Robotics and Automation

By Ben Kehoe, Sachin Patil, Pieter Abbeel, Ken Goldberg. 2015

423 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Mechanical Engineering

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Citations in the corpus

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, background): “…broadly, the ability of robotic systems to learn more quickly by pooling their collective experience has been proposed in a number of prior works, and has been referred to as collective robot learning and an instance of cloud robotics (Inaba et al., 2000; Kuffner, 2010; Kehoe et al., 2013; 2015).”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “Interested readers can refer to [217] for a short survey of this recent practice and to [218] for a more general review of cloud approaches in robotics.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “There have been many proposals for using Cloud computing [24] to help intelligent vehicles, and some of them have already been implemented, such as Cloud-based software update, or training powerful deep learning models [25].”
Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles (2017, background): “Since James Kuffner introduced the term “Cloud Robotics” in 2010, numerous studies have explored the benefits of this approach [22], [23].”
An event-driven manufacturing information system architecture for Industry 4.0 (2017, background): “0 Working Group 2013), Smart Manufacturing Leadership Coalition (Smart Manufacturing Leadership Coalition 2016), Internet of Things (Atzori, Iera, and Morabito 2010), the Industrial Internet (Evans and Annunziata 2012), and cloud robotics and automation (Kehoe et al. 2015).”
A Berkeley View of Systems Challenges for AI (2017, background): “On the other hand, AI systems currently deployed at the edge, such as self-driving cars, drones, and home robots, are increasingly sharing data and leveraging the computational resources available in the cloud to update models and policies [61].”
Transfer Learning in Deep Reinforcement Learning: A Survey (2020, background): “…ﬁeld include robotics learning from demonstrations , where expert demonstrations from humans or other robots are leveraged [135] Another is collaborative robotic training [136], [137], in which knowledge from different robots is transferred by sharing their policies and episodic demonstrations.”
Big Data and cloud computing: innovation opportunities and challenges (2017, neutral): “Big Data (e.g. mobile sensing) require real-time data processing, information extraction and automation for decision support (Han, Liang, and Zhang 2015; Kehoe et al. 2015).”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, background): “In general, these smart machines operate with higher accuracy, greater efficiency and constant working capabilities than humans [95].”
A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges (2019, background): “and memory is integrated into a single standalone system and allows to design (multi-)robot systems with improved energy efficiency, high real-time performance, and low cost [111].”
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems (2019, background): “We focus on cloud computing and cloud robotic technologies [2], which can enhance robotic systems by facilitating”
Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates (2018, background): “A. H. Sayed is with the School of Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. e-mail: ali.sayed@epﬂ.ch. is also useful for collective robot learning (see, for example, [2], [3] and [4]).”