A robot built entirely of carbon nanotubes could someday replace the human hand.
Researchers have made a robotic arm that looks as though it was built by the late 20th century, and they say they’ve achieved a near-perfect replica of the human anatomy, using a technique that could help researchers more accurately test medical interventions for the sick.
The technique, which was developed by scientists at the University of Sheffield and the University in Texas, is called “reinforcement learning,” and it involves teaching a robot to learn by observing another robot’s movements.
The goal is to teach the robot to perform tasks, like manipulating a tool, that require precision and control.
Researchers say that by using the reinforcement learning technique, they can learn to perform a number of tasks that were previously difficult to accomplish using other methods.
The robotic arm is called the “Giant Robot,” after the giant robot that was built in the 1920s by Japanese engineer Hiroshi Katagiri.
It was intended to help surgeons and doctors perform complex surgeries.
Katagiri’s giant robot was designed to help physicians perform surgery on humans with no spinal cord or nerves.
But when it was unveiled in the late 1930s, it was criticized for its high price and the potential for spinal cord injury.
The robotic arm has since been used in several medical procedures, but it has never been able to completely replace the hand of a patient.
In a study published this week in the journal Nature Robotics, the researchers demonstrated that using reinforcement learning to train the Giant Robot to perform complex surgical tasks requires only the presence of a certain amount of electrical current.
The researchers said that by training the robot using this technique, the robot could be trained to perform surgeries on an average of about 20 percent of patients.
They said that in the future, the goal is for the robot arm to be able to perform operations on an even greater percentage of patients, and then to have the ability to perform surgical operations on a third-party robot, such as a robotic exoskeleton.
Researchers hope that the technique will be used to train medical robots to perform more complex tasks, such in robotics.
For example, in the early days of robotics, it might be helpful for a robot arm that was able to do the basic tasks of a human surgeon to be capable of performing more complex surgery tasks, said co-author Dr. Richard J. Stadler, a professor of mechanical engineering and materials science at the California Institute of Technology in Pasadena.
“We’ve got to figure out how to train these machines to do really complex tasks in a much more controlled manner,” he said.
“We’re going to need to train them to be very careful when they perform these tasks, so they don’t go into an accident or have a serious injury.”
Follow Rachael Rettner on Twitter at:@rachaelreyt