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Network latency is a major problem in Cloud Robotics for human robot interactions such as teleoperation. Routing delays can be highly variable in a heterogeneous computing environment, imposing challenges to reliably teleoperate a robot with a closed-loop feedback controller. By sharing Gaussian Mixture Models (GMMs), Hidden Semi- Markov Models (HSMMs), and linear quadratic tracking (LQT) con- trollers between the cloud and the robot. We build a motion recognition, segmentation, and synthesis framework for Cloud Robotic teleoperation; and we introduce a set of latency mitigation network protocols under this framework. We use this framework in experiments with a dynamic robot armmore »
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface DeclutteringThe 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 formore »