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Abstract

Dynamic system-based motor primitives [1] have enabled robots to learn complex tasks ranging from Tennisswings to locomotion. However, to date there have been only
few extensions which have incorporated perceptual coupling to
variables of external focus, and, furthermore, these modifications
have relied upon handcrafted solutions. Humans learn how
to couple their movement primitives with external variables.
Clearly, such a solution is needed in robotics.
In this paper, we propose an augmented version of the dynamic
systems motor primitives which incorporates perceptual coupling
to an external variable. The resulting perceptually driven motor
primitives include the previous primitives as a special case and
can inherit some of their interesting properties. We show that
these motor primitives can perform complex tasks such as Ball-in-a-Cup or Kendama task even with large variances in the initial
conditions where a skilled human player would be challenged. For
doing so, we initialize the motor primitives in the traditional way
by imitation learning without perceptual coupling. Subsequently,
we improve the motor primitives using a novel reinforcement
learning method which is particularly well-suited for motor
primitives.