Robots are becoming a vital ingredient in society. Some of
their daily tasks require dual-arm manipulation skills in the
rapidly changing, dynamic and unpredictable real-world environments
where they have to operate. Given the expertise
of humans in conducting these activities, it is natural to study
humans’ motions to use the resulting knowledge in robotic
control.With this in mind, this work leverages human knowledge
to formulate a more general, real-time, and less taskspecific
framework for dual-arm manipulation. The proposed
framework is evaluated on the iCub humanoid robot and several
synthetic experiments, by conducting a dual-arm pickand-
place task of a parcel in the presence of unexpected obstacles.
Results suggest the suitability of the method towards
robust and generalisable dual-arm manipulation.