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Speech by Neelie Kroes, Vice President of the EC in charge of the Digital Agenda at the "Future and Emerging Technologies Conference ('Science Beyond Fiction')" conference

Standing 41 inches high and weigh 48 1/2 pounds, the iCub is the similar size as a 3-year-old child. It can learn to talk and grasp an order. It also learns by playing with its arms, hands, legs and feet.

THE HEAD CAN MOVE FREELY in the three directions. Its eyes consist of video cameras with a decision of 640×480. Red LEDs can be activated to specify the eyebrows and jaws and reproduce facial expressions.

MICSON BOTH SIDES OF THE HEAD confine sounds, allowing the iCub to respond to vocal information.

THE UPPER ARMS ARE ATTACHED to a shoulder that can spin while a motor pulls a ligament, making the arm shift up or down. A motor is used to turn the elbow joint.

THE WHOLE TORSO IS ONE UNIT mounted on the hips at a solo joint that can not only spin, but also twist slanting, as well as toward the back and ahead.

THE HANDS ARE RESTRICTED by seven motors in the forearm, used for pulling tendons, plus two motors in the hand. All fingers apart from the pinky and ring fingers can be stirred separately.

53 DIFFERENT MOTORS directly or indirectly build the joints and muscles shift. The tendons are cables enclosed in Teflon-coated steel tubes, like the device cable of a bicycle.

THE SKELETON is mainly made of aluminium alloy and steel; its parts are intended for optimal functionality and thus don’t look like human bones.

TACTILE SENSE WILL BE EMBEDDED in the fingertips and other body parts in the structure of a touch sensitive skin. This will let the iCub to make a decision if a demanding object is hard or soft and how hard to hold it.

Many mammals have an astonishing skill to distinguish objects under different conditions. Tasks that may seem effortless are in fact only possible appreciation to the great complication of the mammalian cerebral cortex. One of the most compound stimuli mammals is challenged by are faces.
Natural vision systems stay invariant to things like shifts in position, rotation, and scaling so that  if you were to look at a image of your friend’s face upside down, it wouldn’t cause you to fault them for somebody else.

Due to the rising use of social robots, scientists at present are eager on advancing facial recognition software. In order to do this, researchers have been working on  models of natural visual systems, though, turning the unforced intricacies of the intellectual cortex into hard wires and code is tricky business. A lot of facial recognition software  systems are based on a large vocabulary of facial appearance stored in memory that must be filtered by fake neurons throughout various layers.

Through these means, in variance to factors such as the position and scale can be achieved, but it’s repeatedly at the high cost of raising the number of associations between the layers of the network which results in bulkier hardware. The technology is growing day by day. We must be updated. There are many How to Learn Robotics, where you can join your kids and make them learn Robotics Courses. It will surely help them in their future.

For more:

How to learn robotics | Robotics for kids

See also:

How to learn robotics


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