Scientists at Carnegie Mellon University (CMU) and the University of California, Berkeley, have enabled a low-cost and relatively small legged robot to adapt to obstacles.
The robot uses its vision and an onboard computer to quickly adjust to new situations and master difficult terrain.
The researchers trained it using 4,000 robot clones as they walked and climbed in a simulator, giving the machine six years of experience in one day.
The simulator also retained motor skills acquired in training in a neural network that the team copied to the actual robot.
"This system uses vision and feedback from the body directly as input to output commands to the robot's motors," explained CMU's Ananye Agarwal. "This technique allows the system to be very robust in the real world."
From Carnegie Mellon University News
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