Fog Robotics

Cloud Robotics use wireless networking, Big Data, Cloud Computing, statistical machine learning, open-source, and other shared resources to improve performance in a wide variety of robotic applications. A number of issues arise in communication with far away Cloud data centers, including: 1) the sheer volume of sensory data continues to increase, leading to a higher latency, variable timing, limited bandwidth access, 2) the security and privacy of the data is comprised in communication over heterogeneous networks over the internet.

Fog Robotics enable robots and IoT devices in homes and warehouses to leverage upon nearby Edge resources as well as distant Cloud data centers. Administrative boundaries of resource ownership restrict control of data within domains of trust. The term `Cloud Robotics' indicates the use of network resources at the center of the network (or `Cloud'), while `Fog Robotics' involves the use of networked resources along the Cloud/Edge continuum (or `Fog').

A Fog Robotics approach to deep robot learning distributes resources between Cloud and Edge for training, adaptation, inference serving and updating of deep models to reduce latency and preserve privacy of the data.

Surface decluttering task entails a mobile robot to recognize and grasp objects in the environment, and put them into corresponding bins. Surface decluttering by simulation to reality transfer with HSR: Non-private (public) synthetic images of cluttered floors using 3D meshes of household and machine shop objects (shown on left) are used for large-scale training of deep models on the Cloud. The trained deep models are subsequently adapted to the real objects (shown on right) by learning feature invariant representations with an adversarial discriminator at the Edge. Example of domain invariant object recognition and grasp planning model output on a simulated image on (left) and real image on (right) seen from the robot head camera.