Incremental Learning of Robotic Manipulation Tasks through Virtual Reality Demonstrations

We propose an incremental, modular, and exten- sible method for learning robotic manipulation tasks using a limited number of demonstrations provided in Virtual Reality, while assuming minimal prior information about the objects to be manipulated. The developed framework enables an incremental training process in which the operator first demon- strates specialized tasks to the robotic system, and subsequently more complex tasks, exploiting the skills learned during the previous phases. We illustrate and discuss the method at work considering picking tasks performed by manipulators equipped with multi-fingered sensorized hands. The experimental evalu- ation highlights the feasibility and advantage of the proposed method, particularly in terms of modularity, low number of demonstrations, and reliability of the trained system.