  In an attempt to confer robots with complex manipulation capabilities,
dual-arm anthropomorphic systems have become an important research topic in the
robotics community. Most approaches in the literature rely upon a great
understanding of the dynamics underlying the system's behaviour and yet offer
limited autonomous generalisation capabilities. To address these limitations,
this work proposes a modelisation for dual-arm manipulators based on dynamic
movement primitives laying in two orthogonal spaces. The modularity and
learning capabilities of this model are leveraged to formulate a novel
end-to-end learning-based framework which (i) learns a library of primitive
skills from human demonstrations, and (ii) composes such knowledge
simultaneously and sequentially to confront novel scenarios. The feasibility of
the proposal is evaluated by teaching the iCub humanoid the basic skills to
succeed on simulated dual-arm pick-and-place tasks. The results suggest the
learning and generalisation capabilities of the proposed framework extend to
autonomously conduct undemonstrated dual-arm manipulation tasks.
