English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The growing demand of industrial, automotive and service robots presents achallenge to the centralized Cloud Robotics model in terms of privacy,security, latency, bandwidth, and reliability. In this paper, we present a `FogRobotics' approach to deep robot learning that distributes compute, storage andnetworking resources between the Cloud and the Edge in a federated manner. Deepmodels are trained on non-private (public) synthetic images in the Cloud; themodels are adapted to the private real images of the environment at the Edgewithin a trusted network and subsequently, deployed as a service forlow-latency and secure inference/prediction for other robots in the network. Weapply this approach to surface decluttering, where a mobile robot picks andsorts objects from a cluttered floor by learning a deep object recognition anda grasp planning model. Experiments suggest that Fog Robotics can improveperformance by sim-to-real domain adaptation in comparison to exclusively usingCloud or Edge resources, while reducing the inference cycle time by 4\times tosuccessfully declutter 86% of objects over 213 attempts.

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering