Scientists in Germany have designed a robot dog that can learn to walk from scratch in under an hour. Taking their inspiration from baby animals, researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart, built Morti, a four legged robot, to help them figure out the process behind how four-legged animals learn to walk. "As engineers and roboticists, we sought the answer by building a robot that features reflexes just like an animal and learns from mistakes," says Felix Ruppert, a former doctoral student in the Dynamic Locomotion research group at MPI-IS. "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." Despite its learning to walk in just one hour, Ruppert's robot didn’t know anything of its leg structure or mechanics before it began the learning process. A Bayesian optimization algorithm guides the learning: the measured foot sensor information is matched with target data from the modelled virtual spinal cord running as a program in the robot’s computer. The robot learns to walk by continuously comparing sent and expected sensor information, running reflex loops, and adapting its motor control patterns. The learning algorithm then adapts the control parameters of a Central Pattern Generator (CPG). In humans and animals, these central pattern generators are networks of neurons in the spinal cord that produce periodic muscle contractions without input from the brain. Central pattern generator networks aid the generation of rhythmic tasks such as walking, blinking or digestion. As long as the young animal walks over a perfectly flat surface, CPGs can be sufficient to control the movement signals from the spinal cord. A small bump on the ground, however, changes the walk. Reflexes kick in and adjust the movement patterns to keep the animal from falling. These momentary changes in the movement signals are reversible, or 'elastic', and the movement patterns return to their original configuration after the disturbance. The same holds true for the Labrador-sized robot dog. In fact, it learns and optimises its movement patterns faster than an animal, in about one hour. Morti’s CPG is simulated on a small and lightweight computer that controls the motion of the robot’s legs. This virtual spinal cord is placed on the quadruped robot’s back where the head would be. During the hour it takes for the robot to walk smoothly, sensor data from the robot’s feet are continuously compared with the expected touch-down predicted by the robot’s CPG. If the robot stumbles, the learning algorithm changes how far the legs swing back and forth, how fast the legs swing, and how long a leg is on the ground. During the learning process, the CPG sends adapted motor signals so that the robot henceforth stumbles less and optimises its walking. Remarkably, the virtual spinal cord starts with no explicit knowledge about the robot’s leg design, its motors and springs. Knowing nothing about the physics of the machine, it lacks a robot ‘model’. "Our robot is practically ‘born’ knowing nothing about its leg anatomy or how they work," Ruppert explains. "The CPG resembles a built-in automatic walking intelligence that nature provides and that we have transferred to the robot. The computer produces signals that control the legs’ motors, and the robot initially walks and stumbles. Data flows back from the sensors to the virtual spinal cord where sensor and CPG data are compared. If the sensor data does not match the expected data, the learning algorithm changes the walking behaviour until the robot walks well, and without stumbling. Changing the CPG output while keeping reflexes active and monitoring the robot stumbling is a core part of the learning process." Morti’s computer draws only five watts of power in the process of walking. Industrial quadruped robots from prominent manufacturers, which have learned to run with the help of complex controllers, are much more power hungry. Their controllers are coded with the knowledge of the robot’s exact mass and body geometry – using a model of the robot. They typically draw several tens, up to several hundred watts of power. Both robot types run dynamically and efficiently, but the computational energy consumption is far lower in the Stuttgart model. It also provides important insights into animal anatomy. "We can't easily research the spinal cord of a living animal. But we can model one in the robot," says Alexander Badri-Spröwitz, who co-authored the publication with Ruppert and heads the Dynamic Locomotion Research Group. "We know that these CPGs exist in many animals. We know that reflexes are embedded; but how can we combine both so that animals learn movements with reflexes and CPGs? This is fundamental research at the intersection between robotics and biology. The robotic model gives us answers to questions that biology alone can't answer." Ruppert and his team’s research paper, titled, Learning Plastic Matching of Robot Dynamics in Closed-loop Central Pattern Generators, was published on July 18, 2022 in the prestigious journal Nature Machine Intelligence.