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• W2056518977 abstract "In recent years, research on movement primitives has gained increasing popularity. The original goals of movement primitives are based on the desire to have a sufficiently rich and abstract representation for movement generation, which allows for efficient teaching, trial-and-error learning, and generalization of motor skills (Schaal 1999). Thus, motor skills in robots should be acquired in a natural dialog with humans, e.g., by imitation learning and shaping, while skill refinement and generalization should be accomplished autonomously by the robot. Such a scenario resembles the way we teach children and connects to the bigger question of how the human brain accomplishes skill learning. In this paper, we review how a particular computational approach to movement primitives, called dynamic movement primitives, can contribute to learning motor skills. We will address imitation learning, generalization, trial-and-error learning by reinforcement learning, movement recognition, and control based on movement primitives. But we also want to go beyond the standard goals of movement primitives. The stereotypical movement generation with movement primitives entails predicting of sensory events in the environment. Indeed, all the sensory events associated with a movement primitive form an associative skill memory that has the potential of forming a most powerful representation of a complete motor skill." @default.
• W2056518977 created "2016-06-24" @default.
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• W2056518977 date "2013-04-01" @default.
• W2056518977 modified "2023-09-30" @default.
• W2056518977 title "From dynamic movement primitives to associative skill memories" @default.
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• W2056518977 doi "https://doi.org/10.1016/j.robot.2012.09.017" @default.
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