Robot Toddler Learns to Stand by “Imagining” How to Do It
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Instead of being programmed, a robot uses brain-inspired algorithms to “imagine” doing tasks before trying them in the real world.
Like many toddlers, Darwin sometimes looks a bit unsteady on its feet. But with each clumsy motion, the humanoid robot is demonstrating an important new way for androids to deal with challenging or unfamiliar environments. The robot learns to perform a new task by using a process somewhat similar to the neurological processes that underpin childhood learning.
Darwin lives in the lab of Pieter Abbeel, an associate professor at the University of California, Berkeley. When I saw the robot a few weeks ago, it was suspended from a camera tripod by a piece of rope, looking a bit tragic. A little while earlier, Darwin had been wriggling around on the end of the rope, trying to work out how best to move its limbs in order to stand up without falling over.
Darwin’s motions are controlled by several simulated neural networks—algorithms that mimic the way learning happens in a biological brain as the connections between neurons strengthen and weaken over time in response to input. The approach makes use of very complex neural networks, which are known as deep-learning networks, which have many layers of simulated neurons.
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