Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
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General-purpose autonomous robots must have the ability to combine the available sensorimotor knowledge in order to solve more complex tasks. Such knowledge is often given in the form of movement primitives. In this paper, we investigate the problem of sequencing of movement primitives. We selected nonlinear dynamic systems as the underlying sensorimotor representation because they provide a powerful machinery for the specification of primitive movements. We propose two new methodologies which both ensure that consecutive movement primitives are joined together in a continuous way (up to second-order derivatives). The first is based on proper initialization of the third-order dynamic motion primitives and the second uses online Gaussian kernel functions modification of the second-order dynamic motion primitives. Both methodologies were validated by simulation and by experimentally using a Mitsubishi PA-10 articulated robot arm. Experiments comprehend pouring, table wiping, and carrying a glass of liquid.