Robot Dog Learns to Walk in an Hour After Scientists Build “virtual spinal cord”

“Our robot is practically ‘born’ knowing nothing about its leg anatomy or how they work.”

STUTTGART, Germany — A robot dog with reflexes that teach it how to walk in an hour has been built by scientists. Researchers in Germany say the canine creation, called Morti, learns to walk quickly because it makes good use of its virtual spinal cord.

The German team built the fast-learning four-legged friend in a bid to find out more about how animals in nature learn to walk. Animals are born with muscle coordination networks in their spinal cord but learning precisely how to use their leg muscles and tendons can take time.

Baby animals begin their lives relying on hard-wired spinal cord reflexes. More basic motor control reflexes also help the animal avoid falling and hurting themselves during their first attempts at walking.

Animals must then practice more advanced and precise muscle control until the nervous system adapts to the young creature’s leg muscles and tendons.

“As engineers and roboticists, we sought the answer by building a robot that features reflexes just like an animal and learns from mistakes,” says study first author Felix Ruppert from the Max Planck Institute for Intelligent Systems in Stuttgart in a media release.

“If an animal stumbles, is that a mistake? Not if it happens once. But if it stumbles frequently, it gives us a measure of how well the robot walks.”

How does the robot dog learn this skill?
The robot dog works by using a complex algorithm that guides how it learns. Information from foot sensors is matched with data from the model spinal cord which is running as a program in the robot’s computer.

The robot dog learns to walk by constantly comparing set and expected sensor information, running reflex loops, and adapting the way it regulates its movements. The algorithm adapts control parameters of a Central Pattern Generator (CPG).

In humans and animals, these are networks of neurons in the spinal cord that produce periodic muscle contractions without input from the brain. The pattern generator networks help us walk, blink, and digest food.

Reflexes are involuntary actions triggered by hard-coded pathways that connect sensors in the leg with the spinal cord. As long as an animal walks over a perfectly flat surface, these pattern generators can be sufficient to control the movement signals from the spinal cord.

A small bump changes the walk, reflexes kick in and the creature may have to change its movement patterns in order to avoid falling. These changes are reversible and “elastic,” and movement patterns return to their original configuration after the disturbance.

If the animal does not stop stumbling, despite active reflexes, then the movement patterns must be relearned and made irreversible. In a newborn animal, these pattern generators are not yet adjusted well enough and the animal stumbles around, both on even and uneven terrain.

A small bump changes the walk, reflexes kick in and the creature may have to change its movement patterns in order to avoid falling. These changes are reversible and “elastic,” and movement patterns return to their original configuration after the disturbance.

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