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 lowlatency 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× to successfully declutter 86% of objects over 213 attempts.

The Internet of Things (IoT) represents a new class of applications that can benefit from cloud infrastructure. However, directly connecting smart devices to the cloud has a number of disadvantages and is unlikely to keep up with either the growing speed of the IoT or the diverse needs of IoT applications.We explore these disadvantages and argue that fundamental properties of the IoT prevent the current approach from scaling. What is missing is a well-architected system extending functionality of the cloud and providing seamless interplay among the heterogeneous components in the IoT space. We argue that raising the level of abstraction to a data-centric design-focused around the distribution, preservation and protection of information-better matches the IoT. We present early work on such a distributed platform, called the Global Data Plane (GDP), and discuss how it addresses the problems with the cloud-centric architecture.

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Adaptive Resource-Centric Computing (ARCC) enables a simultaneous mix of high-throughput parallel, real-time, and interactive applications through automatic discovery of the correct mix of resource assignments necessary to achieve application requirements. This approach, embodied in the Tessellation manycore operating system, distributes resources to QoS domains called cells. Tessellation separates global decisions about the allocation of resources to cells from application-specific scheduling of resources within cells. We examine the implementation of ARCC in the Tessellation OS, highlight Tessellation's ability to provide predictable performance, and investigate the performance of Tessellation services within cells.