Kirjahaku

How to develop robots that will be more like humans and less like computers, more social than machine-like, and more playful and less programmed.Most robots are not very friendly. They vacuum the rug, mow the lawn, dispose of bombs, even perform surgery-but they aren't good conversationalists. It's difficult to make eye contact. If the future promises more human-robot collaboration in both work and play, wouldn't it be better if the robots were less mechanical and more social? In How to Grow a Robot, Mark Lee explores how robots can be more human-like, friendly, and engaging.Developments in artificial intelligence-notably Deep Learning-are widely seen as the foundation on which our robot future will be built. These advances have already brought us self-driving cars and chess match-winning algorithms. But, Lee writes, we need robots that are perceptive, animated, and responsive-more like humans and less like computers, more social than machine-like, and more playful and less programmed. The way to achieve this, he argues, is to "grow" a robot so that it learns from experience-just as infants do. After describing "what's wrong with artificial intelligence" (one key shortcoming: it's not embodied), Lee presents a different approach to building human-like robots: developmental robotics, inspired by developmental psychology and its accounts of early infant behavior. He describes his own experiments with the iCub humanoid robot and its development from newborn helplessness to ability levels equal to a nine-month-old, explaining how the iCub learns from its own experiences. AI robots are designed to know humans as objects; developmental robots will learn empathy. Developmental robots, with an internal model of "self," will be better interactive partners with humans. That is the kind of future technology we should work toward.

An industrial robot routinely carrying out an assembly or welding task is an impressive sight. More important, when operated within its design conditions it is a reliable production machine which - depending on the manufacturing process being automated - is relatively quick to bring into operation and can often repay its capital cost within a year or two. Yet first impressions can be deceptive: if the workpieces deviate somewhat in size or position, or, worse; if a gripper slips or a feeder jams the whole system may halt and look very unimpressive indeed. This is mainly because the sum total of the system's knowledge is simply a list of a few variables describing a sequence of positions in space; the means of moving from one to the next; how to react to a few input signals; and how to give a few output commands to associated machines. The acquisition, orderly retention and effective use of knowledge are the crucial missing techniques whose inclusion over the coming years will transform today's industrial robot into a truly robotic system embodying the 'intelligent connection of perception to action'. The use of computers to implement these techniques is the domain of Artificial Intelligence (AI) (machine intelligence). Evidently, it is an essential ingredient in the future development of robotics; yet the relationship between AI practitioners and robotics engineers has been an uneasy one ever since the two disciplines were born.