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Robots are becoming a vital ingredient in society. Some of their daily tasksrequire dual-arm manipulation skills in the rapidly changing, dynamic andunpredictable real-world environments where they have to operate. Given theexpertise of humans in conducting these activities, it is natural to studyhumans' motions to use the resulting knowledge in robotic control. With this inmind, this work leverages human knowledge to formulate a more general,real-time, and less task-specific framework for dual-arm manipulation. Theproposed framework is evaluated on the iCub humanoid robot and severalsynthetic experiments, by conducting a dual-arm pick-and-place task of a parcelin the presence of unexpected obstacles. Results suggest the suitability of themethod towards robust and generalisable dual-arm manipulation.

Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation