Self-healing rescue cyborgs can recover from injuries and continue working without any human help

In a study published Wednesday in Nature, researchers report a robot that can adapt to “injury” by quickly learning to move in spite of damaged limbs. In less than two minutes, the six-legged creature can learn to compensate for two broken legs. Using the same technique, the researchers also created a robotic arm that could continue to move objects in spite of multiple malfunctioning motors.

“Robots will eventually provide tremendous benefits to society, especially if they can complete tasks too dangerous for humans to perform,” study author Jeff Clune of the University of Wyoming told The Post. Many roboticists hope their creations will end up digging for survivors in treacherous rubble and fighting deadly fires in man’s stead. “But robots won’t be effective in those situations if they can’t adapt and continue on after being damaged,” Clune said.

A robot can easily overcome damage if it’s able to work through the different ways it can move its body. By using a learning algorithm to try out different ways of moving, a robot can come up with a new gait. But that usually takes hours.

“The robot has to search intelligently through the space of all possible behaviors,” Clune said. “It’s larger than the number of molecules on planet earth, it’s like finding one of a few needles in a field of haystacks.”

To speed up the process when performance is crucial, Clune and his collaborators gave their robots what he calls a “simulated childhood.” Before being operated, they spend several hours going through simulations.


“It plays for a while,” Clune said. “It learns lots of ways to move its body, just like a child saying, ‘Oh, I can walk on my tippy toes, I can hop on one foot.'”

So just like a human or other animal, the injured robot is able to find a solution without working from scratch: It has an intuitive sense of the different motions its body can go through, so it adapts to loss of limbs and motors much more quickly than it otherwise could.


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