Asamblarea roboților de tranzacționare, Arătând roboților cum să-și facă treburile
Republicată de Platon Training interactive robots may one day be an easy job for everyone, even those without programming expertise.
Roboticists are developing automated robots that can asamblarea roboților de tranzacționare new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores.
Software Arduino montator Acesta este un limbaj de nivel scăzut cât mai aproape de codul mașinii.
In the workplace, you could train robots like new employees, showing them how to perform many duties. Making progress on that vision, MIT researchers have designed a system that lets these types of robots learn complicated tasks that would otherwise stymie them with too many confusing rules.
În ce diferă robotica pentru copii de cea profesională?
One such task asamblarea roboților de tranzacționare setting a dinner table under certain conditions. In their work, the researchers compiled a dataset with information about how eight objects — a mug, glass, spoon, fork, knife, dinner plate, small plate, and bowl — could be placed on a table in various configurations.
A robotic arm first observed randomly selected human demonstrations of setting the table with the objects. Then, the researchers tasked the arm with automatically setting a table in a specific configuration, in real-world experiments and in simulation, based on what it had seen. To succeed, the robot had to weigh many possible placement orderings, even when items were purposely removed, stacked, or hidden.
Normally, all of that would confuse robots too much.
Factory workers can teach a robot to do multiple complex assembly tasks. Domestic robots can learn how to stack cabinets, load the dishwasher, or set the table from people at home. Learning to set a table by observing demonstrations, is full of uncertain specifications.
Nu trebuie să ne crezi pe cuvânt
Present approaches to planning are not capable of dealing with such uncertain specifications. In short, robots never fully learn right from wrong. The belief itself can then be used to dish out rewards and penalties. The researchers defined templates in LTL that model various time-based conditions, such as what must happen now, must eventually happen, and must happen until something else occurs.
Roboți de tranzacționare în tranzacționare. Piața mondială a roboticii: Dimensiunea pieței de la 15 la 30 de miliarde de dolari diferența în estimări față de ceea ce diferiți experți consideră roboticăținând seama de principalele segmente - robotică industrială și de serviciu roboți militari, de uz casnic, în scopuri educaționale, pentru a ajuta persoanele cu dizabilități și jucăriile robotizate volumul pieței mondiale robotica de serviciu este estimată la 5,3 miliarde de dolari. Vânzări de roboți industriali din până în a crescut de la de mii de unități.
Each formula encoded a slightly different preference — or specification — for setting the table. That probability distribution becomes its belief.
De unde știi dacă un copil are o înclinație spre robotică?
Following criteria The researchers also developed several criteria that guide the robot toward satisfying the entire belief over those candidate formulas. One, for instance, satisfies the most likely formula, which discards everything else apart from the template with the highest probability. Others satisfy the largest number of unique formulas, without considering their overall probability, or they satisfy several formulas that represent highest total probability.
Another simply minimizes error, so the system ignores formulas with high probability of failure. Designers can choose any one of the four criteria to preset before training and testing.
Each has its own tradeoff between flexibility and risk aversion. The choice of criteria depends entirely on the task.
In safety critical situations, for instance, a designer may choose to limit possibility of failure. But where consequences of failure are not as severe, designers can choose to give robots greater flexibility to try different approaches.
Care este diferența dintre robotică pentru copii și profesioniști?
In simulations asking the robot to set the table in different configurations, it only made six mistakes out of 20, tries. In real-world demonstrations, it showed behavior similar to how a human would perform the task. Then, when the fork was revealed, it would set the fork in the proper place.