Cloud robotics: architecture, challenges and applications

By Guoqiang Hu, Wee Peng Tay, Yonggang Wen. IEEE Network, 2012

537 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f80feecb8e7d91267bed7e5c24afe0bfaf30ff61

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Ken Goldberg
2. Pieter Abbeel
3. Ben Kehoe
4. Sergey Levine
5. Lihong Li
6. Michael L. Littman
7. Chelsea Finn
8. Pawel Wawrzynski

In Control and Systems Engineering

1. Ken Goldberg
2. Pieter Abbeel
3. Sergey Levine
4. Sachin Patil
5. Ben Kehoe
6. Chelsea Finn
7. Vikash Kumar
8. Silvio Savarese

In Mechanical Engineering

1. Pieter Abbeel
2. Ken Goldberg
3. Sachin Patil
4. Ben Kehoe

Citations in the corpus

• Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, background): “Under a major European Union grant, the RoboEarch research team developed a series of system architectures for service robotics [30], [53], developing Cloud networking [74], [87], and computing resources [78] to generate 3D models of environments, speech recognition, and face recognition [148].”
• A Survey of Research on Cloud Robotics and Automation (2015, methodology): “Deep learning is a technique using many-layered neural networks that can take advantage of Big Data [47], and has been used for computer vision [94], [139] and grasping [101].”