Cloud Robotics

From P2P Foundation
Jump to navigation Jump to search



"Cloud Robotics is the application of the cloud computing concept to robots. This means using the Internet to augment the robots capabilities by off-loading computation and providing services on demand.

Being connected to the cloud also helps robots to collaborate with other machines, smart objects and humans. Through this collaboration, robots transcend their physical limitations and become more useful and capable since they can delegate parts of their tasks to more suitable parties.

By combining the increased communication capabilities to the ability and flexibility of running and storing part of their intelligence (i.e. software, behaviours and apps)on the infrastructure, smart objects and robots become augmented and constitute a new and revolutionary concept and the future of robotic intelligence: Cloud Robotics." (

2. Ken Goldberg:

"Although robots have been on the Internet for 20 years, in 2010 James Kuffner, a brilliant researcher at Google, coined the term “Cloud Robotics.” The Cloud isn’t just a new name for the Internet. It’s a new paradigm that uses the Internet in new ways.


Until now, Robots have been viewed as self-contained systems with limited computation and memory. Cloud Robotics suggest an exciting alternative where robots access and exchange data and code via wireless networking.


Cloud Robotics will build on related effort including the “Internet of Things,” “IBM’s Smarter Planet,” General Electric’s vision of the “Industrial Internet,” and Siemens’ concept of “Industry 4.0.” These approaches have enormous potential but also open a Pandora’s Box of issues related to privacy and security. But when robots have their heads in the clouds, the sky’s the limit." (


Ken Goldberg:

"The Five Elements of Cloud Robotics are:

Big Data: indexing a global library of images, maps, and object data,

Cloud Computing: grid computing on demand for statistical learning, and motion planning,

Open-Source: humans sharing robot code, data, algorithms, and hardware designs,

Collective Robot Learning: robots sharing trajectories, control policies, and outcomes that can be analyzed with statistical machine learning methods and

Crowdsourcing and call centers: offline and on-demand human guidance for evaluation, learning, and error recovery." (