![]() ![]() Now imagine a “Robot Goggles” application, Kuffner suggested a robot would send images of what it is seeing to the cloud, receiving in return detailed information about the environment and objects in it. You snap a picture of a painting at a museum or a public landmark and Google sends you information about it. Even more promising, the robots could turn to cloud-based services to expand their capabilities.Īs an example, he mentioned the Google service known as Google Goggles. As a result, sophisticated systems like humanoid robots need to carry powerful computers and large batteries to power them.Īccording to Kuffner, cloud robotics could offload CPU-heavy tasks to remote servers, relying on smaller and less power-hungry onboard computers. Kuffner described the possibilities of cloud robotics at the IEEE International Conference on Humanoid Robots, in Nashville, Tenn., this past December. Embracing the cloud could make robots “lighter, cheaper, and smarter,” he said in his talk, which created much buzz among attendees.įor conventional robots, every task-moving a foot, grasping an object, recognizing a face-requires a significant amount of processing and preprogrammed information. The robot could simply send an image of the cup to the cloud and receive back the object’s name, a 3-D model, and instructions on how to use it, says James Kuffner, a professor at Carnegie Mellon currently working at Google who coined the term “cloud robotics.” Imagine a robot that finds an object that it's never seen or used before-say, a plastic cup. This approach, which some are calling "cloud robotics," would allow robots to offload compute-intensive tasks like image processing and voice recognition and even download new skills instantly, Matrix-style. Several research groups are exploring the idea of robots that rely on cloud-computing infrastructure to access vast amounts of processing power and data. In the first “Matrix” movie, there’s a scene where Neo points to a helicopter on a rooftop and asks Trinity, “Can you fly that thing?” Her answer: “Not yet.” Then she gets a “pilot program” uploaded to her brain and they fly away.įor us humans, with our non-upgradeable, offline meat brains, the possibility of acquiring new skills by connecting our heads to a computer network is still science fiction. Several numerical examples demonstrate that the Adaptive-FCA-OLS algorithm has better robustness to noise and to the size of the initial neighborhood than other recently developed neighborhood detection methods in the identification of binary CA.Cloud Robotics will make robots smaller, cheaper, and smarter, says a Google researcher. By introducing a new criteria and three new techniques, this paper proposes a new adaptive fast CA orthogonal-least-square (Adaptive-FCA-OLS) algorithm, which cannot only adaptively search for the correct neighborhood without any preset tolerance but can also considerably reduce the computational complexity and memory usage. This is true particularly for a large initial neighborhood where there are few significant terms, and this will be demonstrated by an example in this paper. Many authors have suggested procedures based on the removal of redundant neighbors from a very large initial neighborhood one by one to find the real model, but this often induces ill conditioning and overfitting. An important step in the identification of cellular automata (CA) is to detect the correct neighborhood before parameter estimation.
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