Cloud Robotics is a paradigm where multiple robots are connected to cloud services via Internet to access “unlimited” computation power, at the cost of network communication. However, due to limitations such as network latency and variability, it is difficult to control dynamic, human compliant service robots directly from the cloud. In this work, we combine cloud robotics with an agile edge device to build a Fog Robotic system by leveraging an asynchronous protocol with a “heartbeat” signal. We use the system to enable robust teleoperation of a dynamic self-balancing robot from the cloud. We use the system to pick up boxes from static locations, a task commonly performed in warehouse logistics. To make cloud teleoperation more intuitive and efficient, we program a cloud-based image based visual servoing (IBVS) module to automatically assist the cloud teleoperator during the object pickups. Visual feedbacks, including apriltag recognition and tracking, are performed in the cloud to emulate a Fog Robotic object recognition system for IBVS. We demonstrate the feasibility of a dynamic real-time automation system using this cloud-edge hybrid design, which opens up possibilities of deploying dynamic robotic control with deep-learning recognition systems in Fog Robotics. Finally, we show that Fog Robotics enables the self-balancing service robot to pick up a box automatically from a person under unstructured environments.

In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Berkeley Robotics and Automation as a Service (Brass), a RAaaS prototype that allows robots to access a remote server that hosts a robust grasp-planning system (Dex-Net 1.0) that maintains data on hundreds of candidate grasps on thousands of 3D object meshes and uses perturbation sampling to estimate and update a stochastic robustness metric for each grasp. Results suggest that such a system can increase grasp reliability over naive locally-computed grasping strategies with network latencies of 30 and 200 msec for servers 500 and 6000 miles away, respectively. We also study how the system can use execution reports from robots in the field to update grasp recommendations over time.