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. Particularly, the proposed architecture first learns the dynamics underlying the execution of different primitive skills. These are harvested in a one-at-a-time fashion from human demonstrations, making dual-arm systems accessible to non-roboticists-experts. Then, the framework exploits such knowledge simultaneously and sequentially to confront complex and novel scenarios.

Current works in the literature deal with the challenges arising from particular dual-arm applications in controlled environments. Thus, the novelty of this work lies in (i) learning a set of primitive skills in a one-at-a-time fashion, and (ii) endowing dual-arm systems with the ability to reuse their knowledge according to the requirements of any commanded task, as well as the surrounding environment. The potential of the proposed framework is demonstrated with several experiments involving synthetic environments, the simulated and real iCub humanoid robot. Apart from evaluating the performance and generalisation capabilities of the different primitive skills, the framework as a whole is tested with 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.