  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 task-specific framework for dual-arm manipulation. The
proposed framework is evaluated on the iCub humanoid robot and several
synthetic experiments, by conducting a dual-arm pick-and-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.
