The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.
The author was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments.
In the work, they propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud.
Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation.
The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge.
Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems.
Along with this, the self-driving experimental results also demonstrate that PARL is capable of improving learning effects with data collaboration of local robots.
RoboEarth [13] was funded by the European Union's Seventh Framework Programme for research, technological development projects, specifically to explore the field of cloud robotics.
RoboEarth's World-Wide-Web style database stores knowledge generated by humans – and robots – in a machine-readable format.
The RoboEarth Cloud Engine includes support for mobile robots, autonomous vehicles, and drones, which require much computation for navigation.
In addition, the computing environments are tightly interconnected with each other and have a high bandwidth connection to the RoboEarth knowledge repository.
And the project is supported by the National Science Foundation, the Office of Naval Research, the Army Research Office, Google, Microsoft, Qualcomm, the Alfred P. Sloan Foundation and the National Robotics Initiative, whose goal is to advance robotics to help make the United States more competitive in the world economy.
COALAS [21] is funded by the INTERREG IVA France (Channel) – England European cross-border co-operation programme.
A library for ROS that is a pure Java implementation, called rosjava, allows Android applications to be developed for robots.
[22] DAVinci Project is a proposed software framework that seeks to explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop.
[23] The project aims to build a cloud computing environment capable of providing a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data co-operatively across the robotic ecosystem.
C2RO published a peer-reviewed paper at IEEE PIMRC17 showing its platform could make autonomous navigation and other AI services available on robots- even those with limited computational hardware (e.g. a Raspberry Pi)- from the cloud.
[25] C2RO eventually claimed to be the first platform to demonstrate cloud-based SLAM (simultaneous localization and mapping) at RoboBusiness in September 2017.
As wireless communication technology developed, new forms of experimental "remote brain" robots were developed controlled by small, onboard compute resources for robot control and safety, that were wirelessly connected to a more powerful remote computer for heavy processing.
It marked the availability of high-capacity networks, low-cost computers and storage devices as well as the widespread adoption of hardware virtualization and service-oriented architecture.