Robot that teaches dog;
To evade predators, a baby giraffe or foal must learn to move as quickly as possible on its legs. Animals have networks for coordinating their muscles in their spinal cords from birth. Yet, it takes some time to master the perfect coordination of the tendons and muscles of the legs. Animal young first rely significantly on hardwired reflexes in the spinal cord. The animal’s motor control reflexes, however considerably more primitive, enable it avoid falling and harming itself when it first tries to walk. After that, it is necessary to practice more complex and exact muscle control until the nervous system has eventually become fully attuned to the young animal’s leg muscles and tendons. The young animal is no longer floundering around aimlessly; it can now keep up with the adults.
To better understand how animals learn to walk and learn from mistakes, scientists at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart undertook a study. They created a four-legged, canine-sized robot to aid in their analysis of the situation.
According to Felix Ruppert, a former doctorate student in the Dynamic Locomotion research group at MPI-IS, “As engineers and roboticists, we sought the answer by constructing a robot that displays reflexes just like an animal and learns from mistakes.” “Is it a mistake if an animal stumbles? Not if it just occurs once. However, if it trips up regularly, it tells us how well the robot walks.”
“Learning Plastic Matching of Robot Dynamics in Closed-loop Central Pattern Generators,” which will be released on July 18, 2022 in the journal Nature Machine Intelligence, has Felix Ruppert as its first author.
Virtual spinal cord optimization using learning algorithm
Ruppert’s robot makes effective use of its intricate leg mechanics after learning to walk in just one hour. The learning is guided by a Bayesian optimization algorithm, which compares the target data from the modeled virtual spinal cord running as a program in the robot’s computer with the measured foot sensor information. By executing reflex loops, comparing sent and expected sensor data, and modifying its motor control patterns, the robot gradually learns to walk.
A Central Pattern Generator’s control settings are adjusted by the learning algorithm (CPG). These central pattern generators in both humans and animals are networks of neurons in the spinal cord that cause regular muscle contractions without brain input. Networks with central pattern generators help create rhythmic actions like blinking, walking, or digestion. Furthermore, brain connections that are hard-wired and connect sensors in the leg to the spinal cord cause reflexes, which are involuntary motor control actions.
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. But if the animal does not stop stumbling over many cycles of movement—despite active reflexes—then the movement patterns must be relearned and made “plastic,” i.e., irreversible. In the newborn animal, CPGs are initially not yet adjusted well enough and the animal stumbles around, both on even or uneven terrain. But the animal rapidly learns how its CPGs and reflexes control leg muscles and tendons.
The same holds true for the Labrador-sized robot-dog named Morti. Even more, the robot optimizes 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. The adjusted motion also affects how well the robot can utilize its compliant leg mechanics. During the learning process, the CPG sends adapted motor signals so that the robot henceforth stumbles less and optimizes its walking. In this framework, the virtual spinal cord has 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 behavior 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.”
Power-saving robot dog control
Only five watts are used by Morti’s computer while it is moving. Industrial quadruped robots from well-known manufacturers are substantially more power-hungry since they have become adept at moving with the aid of sophisticated controllers. Using a model of the robot, their controls are programmed with information of the precise mass and body geometry. They typically consume a few tens of watts to several hundred watts. Both robot kinds function dynamically and effectively, but the Stuttgart model uses far less computational power. Additionally, it offers crucial insights on animal anatomy.
“A living animal’s spinal cord is difficult to study. However, we can simulate one in the robot “explains Alexander Badri-Spröwitz, who leads the Dynamic Locomotion Research Group and co-authored the article with Ruppert. “These CPGs are recognized in a wide variety of mammals. We are aware that reflexes are ingrained, but how can we combine the two to enable animals to learn actions through both CPGs and reflexes? This is fundamental investigation into the interface between biology and robotics. The robotic paradigm provides us with solutions to issues that biology alone is unable to address.”