Robot Dog Learned to Walk in Just One Hour

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Robot Dog Learned to Walk in Just One Hour

Discovering how animals learn to walk and learn from stumbling was the goal of a study carried out by researchers at the Max Planck Institute for Intelligent Systems (MPI-IS). They created a four-legged, canine-sized robot to aid them in comprehending the specifics.

A newborn giraffe or foal needs to learn to walk as fast as possible to avoid predators. Animals are born with a network of muscle coordination networks in their spinal cord. But it takes time to learn to precisely coordinate the leg muscles and tendons. At first, baby animals rely heavily on innate spinal reflexes. Motor control reflexes are a little more basic, but they help animals avoid falling and hurting themselves during their first attempts to walk. After that, more advanced and precise muscle control needs to be practiced until the nervous system finally adapts well to the young animal’s leg muscles and tendons. After that, uncontrolled stumbles are left behind and the young animal can keep up with the adults.

Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) conducted a study to find out how animals learn to walk and learn from stumbling. They built a four-legged, dog-sized robot to help them understand the details.

“As engineers and roboticists, we tried to find the answer by making a robot that reflexes and learns from mistakes just like an animal,” says Felix Ruppert, formerly a PhD student in the Dynamic Motion research group at MPI-IS. “If an animal stumbles, is it a mistake? Not if it happens once. But the fact that it stumbles often provides a benchmark for how well the robot walks.”

Felix Ruppert is the first author of the study published yesterday in Nature Machine Intelligence.

Robot Dog Learned to Walk in Just One Hour 1
Photo: Felix Ruppert/Dynamic Movement Group

Rupert’s robot, which makes good use of complex leg mechanics, learned to walk in just one hour. A Bayesian optimization algorithm guides the learning: Information measured by a sensor in the foot is matched with target data from a virtual spinal cord model running as a program on the robot’s computer. The robot learns to walk by constantly comparing the sent and expected sensor information, running reflex loops and adapting to motor control patterns.

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