  Humans exhibit outstanding learning, planning and adaptation capabilities
while performing different types of industrial tasks. Given some knowledge
about the task requirements, humans are able to plan their limbs motion in
anticipation of the execution of specific skills. For example, when an operator
needs to drill a hole on a surface, the posture of her limbs varies to
guarantee a stable configuration that is compatible with the drilling task
specifications, e.g. exerting a force orthogonal to the surface. Therefore, we
are interested in analyzing the human arms motion patterns in industrial
activities. To do so, we build our analysis on the so-called manipulability
ellipsoid, which captures a posture-dependent ability to perform motion and
exert forces along different task directions. Through thorough analysis of the
human movement manipulability, we found that the ellipsoid shape is task
dependent and often provides more information about the human motion than
classical manipulability indices. Moreover, we show how manipulability patterns
can be transferred to robots by learning a probabilistic model and employing a
manipulability tracking controller that acts on the task planning and execution
according to predefined control hierarchies.
