Date: 8 months ago   Category: Hi-Tech

The robot from MIT "sees" and dexterously takes objects of any form


Engineers from MIT have taught to be enough and move the robot objects of any irregular shapes - even such with which
didn't learn to cope the car.
the Group of engineers of Laboratory of cybernetics of the Massachusetts Institute of Technology has developed the Dense Object Nets (DON) system which allows robots to estimate a form of objects rather precisely to capture them, to hold and make simple manipulations without preliminary training at concrete type of objects.

That the system has made the three-dimensional map of an object, the hand manipulator turns the cameras fixed on her under different corners; the neuronet recognizes the images arriving from the camera and determines exact coordinates of each of a set of points on which an object is displayed.

Having processed these coordinates, the system classifies objects and their parts and analyzes the spatial relations between them. To show it, founders of DON have shot video on which the manipulator operated by system sorts boots by flowers and on command lifts them in the specified way - for a uvula, - even if never I faced a boot of concrete type earlier. In it he is similar on the DexNet robot from engineers from the University of California in Berkeley though between the algorithms operating them there is a big difference: DexNet is only able to be enough objects, but isn't able to take them in the set place and doesn't distinguish types of objects.

"The systems using other approaches to identification of objects experience difficulties when the subject is focused in relation to the robot in the unusual way. For the majority to lift a cup for the handle - an impracticable task just because the mug can lie, for example, on one side or stand upside down", - Lucas Manuelli, one of DON developers explains.

the Robots able to distinguish, sort objects and is correct to treat them, can find application in warehouses; founders of DON expect that their development will draw attention of large retailers, such as Amazon and Walmart. Besides, robots which well are guided in the world of things can be useful at home - as housekeepers.

Exists two popular approaches to training of cars in manipulations with physical objects. The first means highly specialized training at concrete examples, the second - creation of a universal algorithm of capture. In the first case the car will cope only with a limited set of tasks - we will tell, will be able to lift balls, - but won't cope with a cube until it is trained to take cubes. Universal ways to be enough a thing seldom happen effective, but also, with the subject taken in the "universal" way it is difficult to make exact manipulations: for example, to put on the set place. DON copes even with unfamiliar objects and allows to predict precisely in what point there will be their concrete points after movement. Founders of DON will make

the report on conferences on training of robots in Zurich in October; briefly telling about development

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