Abstract

Social robots must adapt to dynamic environments, human interaction partners and challenging new stringent tasks. Their inner software should be designed and deployed carefully because slight changes in the robot's requirements can have an important impact in the existing code. This paper focus on the design and implementation of a lightweight middleware for networked robotics called \textit{Nerve}, which guarantees the scalability and quality-of-service requirements for this kind of real-time software. Its benefits have been proved through its use in a Robot Learning by Imitation control architecture, but its design guidelines are general enough to be also applied with common distributed and real-time embedded applications.

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