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

使用 “Fog Robotics” 方法，将计算、存储和网络资源在云和边缘之间进行分布式管理，从而提高对机器人的深度学习训练并保证了隐私和安全性。通过将模型应用于物体整理等任务，实验结果表明相比于独立使用云或边缘资源，Fog Robotics 方法可以通过模拟到真实领域适应来提高性能，同时将推理周期时间缩短了 4 倍并在 213 次尝试中成功清理了 86% 的物体。