Abstract-- To circumvent persistent connectivity to the cloud infrastructure, the current emphasis on computing at network edge devices in the multi-robot domain is a promising enabler for delay-sensitive jobs, yet its adoption is rife with challenges. This paper proposes a novel utility-aware dynamic task offloading strategy based on a multi-edge-robot system that takes into account computation, communication, and task execution load to minimize the overall service time for delay-sensitive applications. Prior to task offloading, continuous device, network, and task profiling are performed, and for each task assigned, an edge with maximum utility is derived using a weighted utility maximization technique, and a system reward assignment for task connectivity or sensitivity is performed. A scheduler is in charge of task assignment, whereas an executor is responsible for task offloading on edge devices. Experimental comparisons of the proposed approach with conventional offloading methods indicate better performance in terms of optimizing resource utilization and minimizing task latency. Figure 1: Overview of the proposed utility-aware offloading I.

Establishing a communication bridge by transferring data driven from different embedded sensors via internet or reconcilable network protocols between enormous number of distinctively addressable objects or "things", is known as the Internet of Things (IoT). IoT can be amalgamated with multitudinous objects such as thermostats, cars, lights, refrigerators, and many more appliances which will be able to build a connection via internet. Where objects of our diurnal life can establish a network connection and get smarter with IoT, robotics can be another aspect which will get beneficial to be brought under the concept of IoT and is able to add a new perception in robotics having "Mechanical Smart Intelligence" which is generally called "Internet of Robotic Things" (IoRT). A robotic arm is a part of robotics where it is usually a programmable mechanical arm which has human arm like functionalities. In this paper, IoRT will be represented by a 5 DoF (degree of freedoms) Robotic Arm which will be able to communicate as an IoRT device, controlled with heterogeneous devices using IoT and "Cloud Robotics".

In this paper, we report on our use of cloud-robotics solutions to teach a Robotics Applications Programming course at Zurich University of Applied Sciences (ZHAW). The usage of Kubernetes based cloud computing environment combined with real robots -- turtlebots and Niryo arms -- allowed us to: 1) minimize the set up times required to provide a Robotic Operating System (ROS) simulation and development environment to all students independently of their laptop architecture and OS; 2) provide a seamless "simulation to real" experience preserving the exciting experience of writing software interacting with the physical world; and 3) sharing GPUs across multiple student groups, thus using resources efficiently. We describe our requirements, solution design, experience working with the solution in the educational context and areas where it can be further improved. This may be of interest to other educators who may want to replicate our experience.

Improving business productivity and performance majorly depends on collecting the data and effectively analyzing it. With the growing popularity of IoT, there will be an obvious surge in data that will require better infrastructure and smarter Data Science approaches. Internet of Things (IoT) is not a separate piece of the latest technology but a convergence of various complementing technologies that contribute towards production optimization, predictive maintenance, asset monitoring and management, new products and services, etc of the businesses. It is expected that, by 2020, more than 20 billion devices will be connected. This depicts the enormous volumes of data that will be generated from these devices.

C2RO Cloud Robotics is looking for an outstanding AI Research Lead who is willing to solve the latest industry challenges in the multidisciplinary field of AI, computer vision and cloud robotics. C2RO is a well-funded Montreal based high-tech software startup focused on developing AI-enabled software applications such as robotic vision and multi-robot collaboration solutions for mobile robots and other smart devices. Our unique cloud robotics SaaS platform allows us to process sensor data at real-time in a scalable and reliable manner which dramatically augment the cognitive, perception, and collaboration capabilities of robots in unstructured and complex environment. Having to provide for a rapidly growing industry with evolving needs, C2RO must push the frontier of robotics with an ever-improving portfolio of Cloud AI features. As such, it is crucial that our technology remains the most advanced solution on the market, an achievement made possible by a strong R&D team.

Cloud Robotics is a term that was popularized by James Kuffner after he brought together researchers from different relevant fields (robotics, machine learning, and computer vision) to assist in coming up with the initial Cloud Robotics concept. Cloud robotics, as the name suggests is bringing together cloud computing and robotics. In essence, taking all the benefits of cloud computing and finding ways to apply them to robot software and robotics. The past couple of years have established cloud computing as the technology of now and the future. In 2017, spending on cloud services was $153.5bn, and this is expected to rise by 21.1% in 2018 to $184.4bn.

"The Latest Research Report 5G & Cloud Robotics for Industrial IoT Market - Global Industry Analysis, Size, Share, Growth, Trends, and Forecast 2017 - 2025 provides information on pricing, market analysis, shares, forecast, and company profiles for key industry participants. Robotics have become a crucial, time as well as energy efficient elements of modern industry. Development of smart robots have supported and efficiently enabled the entire processes including vehicle assembly to be carried out automatically. However, though the development of robots/robotics proved to be beneficial for various industries, it brought with it the two major challenges namely the cost and substantial amount of intelligence robots requires to function properly. Therefore, the challenge was to build simplified smart robotic system at affordable cost.

If you walk into the computer science building at Stanford University, Mobi is standing in the lobby, encased in glass. He looks a bit like a garbage can, with a rod for a neck and a camera for eyes. He was one of several robots developed at Stanford in the 1980s to study how machines might learn to navigate their environment--a stepping stone toward intelligent robots that could live and work alongside humans. He worked, but not especially well. The best he could do was follow a path along a wall.

This article originally appeared in the Journal of Economic Perspectives, Vol. 29, No. 3 (Summer 2015). We thank the American Economic Association for giving us permission to reproduce it here. About half a billion years ago, life on earth experienced a short period of very rapid diversification called the "Cambrian Explosion." Many theories have been proposed for the cause of the Cambrian Explosion, with one of the most provocative being the evolution of vision, which allowed animals to dramatically increase their ability to hunt and find mates (for discussion, see Parker 2003). Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots depend--particularly computing, data storage, and communications--have been improving at exponential growth rates. Two newly blossoming technologies--"Cloud Robotics" and "Deep Learning"--could leverage these base technologies in a virtuous cycle of explosive growth. In Cloud Robotics--a term coined by James Kuffner (2010)--every robot learns from the experiences of all robots, which leads to rapid growth of robot competence, particularly as the number of robots grows.

Humans use language to tap into the knowledge of others and learn skills faster. This helps us hone our intuition and go through our daily activities more efficiently. Inspired by this, Google Research, DeepMind (its UK artificial intelligence lab), and Google X have decided to allow their robots share their experiences. Sharing the learning process among multiple robots, the research team has considerably expedited general-purpose skill acquisition of robots. Using an artificial neural network, we can teach a robot to achieve a goal by analyzing the result of its previous experiences.