Helping robots remember: Hyperdimensional computing theory could change the way AI works

Maryland research could fundamentally alter and improve the basic artificial intelligence task of sensorimotor representation — how agents like robots translate what they sense into what they do The Houston Astros’ José Altuve steps up to the plate on a 3-2 count, studies the pitcher and the situation, gets the go-ahead from third base, tracks the ball’s release, swings … and gets a single up the middle. Just another trip to the plate for the three-time American League batting champion. Could a robot get a hit in the same situation? Not likely. Altuve has honed natural reflexes, years of experience, knowledge of the pitcher’s tendencies, and an understanding of the trajectories of various pitches. What he sees, hears, and feels seamlessly combines with his brain and muscle memory to time the swing that produces the hit. The robot, on the other hand, needs to use a linkage system to slowly coordinate data from its sensors with its motor capabilities. And it can’t remember a thing. Strike three! But there may be hope for the robot. A paper by University of Maryland researchers just published in the journal Science Robotics introduces a new way of combining perception and motor commands using the so-called hyperdimensional computing theory, which could fundamentally alter and improve the basic artificial intelligence (AI) task of sensorimotor representation — how agents like robots translate what they sense into what they do. “Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception” was written by computer science Ph.D. students Anton Mitrokhin and Peter Sutor, Jr.; Cornelia Fermüller, an associate research scientist with the University of Maryland Institute for Advanced Computer Studies; and Computer Science Professor Yiannis Aloimonos. Mitrokhin and Sutor are advised by Aloimonos. Integration is the most important challenge facing the robotics field. A robot’s sensors and […]

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