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A robot dog named Morti has taught itself to walk, one hour after its first step. The robot learned just like animals in the wild<\/a>: by tripping and stumbling until it understood how to balance on its limbs.<\/p>\n Morti was developed by researchers at the Max Planck Institute for Intelligent Systems as a way for scientists to closely study how animals learn to walk. With Morti, researchers could measure the forces and torques, and muscle power, of each limb \u2013 something that would\u2019ve been much more difficult to do in a live organism, said Felix Ruppert<\/a>, a PhD student and first author of the new study.<\/p>\n In building the Labrador-sized robot dog, Ruppert and the team first needed to computerise the mechanism by which animals (and humans) learn to walk.<\/p>\n Walking, like blinking and breathing, are called rhythmic tasks because they use the same muscle movements repeated throughout the activity. Rhythmic tasks aren\u2019t co-ordinated in the brain, but are controlled by networks of neurones, collectively called a Central Pattern Generator (CPG).<\/p>\n Our CPG for walking is found in our spinal cord, as this is what controls the muscle contractions in our legs that take us forward, one step at a time.\u00a0When we trip or stumble over rough terrain, we don\u2019t immediately stop walking. This is because the spinal CPG can control our legs\u2019 reflexes without needing to check with the brain about how to proceed.<\/p>\n This also means an animal\u2019s ability to learn how to walk is located not in the brain, but in the spinal cord. So, Morti had to be given an algorithm that acted like a computerised spinal cord.<\/p>\n \u201cCPGs in nature are networks of nerve cells, while in robotics it can be represented as a set of nodes,\u201d said Ruppert. \u201cMorti\u2019s CPG nodes are connected to nodes in each leg, so that [triggering one] node creates the back-and-forth oscillation of the limb.\u201d<\/p>\n