Learning Robotic Manipulation Tasks based on Incremental Demonstrations in a Virtual Environment

The ability to grasp and manipulate objects is crucial for performing several complex tasks and it is highly desirable to transfer this skill effectively and naturally to robotic systems. Learning by demonstration provides a particularly interesting and promising technique to address this problem, as it allows us to leverage the guidance of the demonstrations provided by an expert to speed up task learning. In this work, we tackle the problem of learning manipulation tasks by demonstration in a virtual environment. Our aim is to develop an incremental, generalizable, and robust method for learning robotic manipulation tasks using a limited number of demonstrations, while assuming minimal information about the objects to be manipulated. The developed method combines imitation learning and reinforcement learning by proposing an incremental approach in which the operator first demonstrates specialized tasks to the robotic system, and subsequently more complex tasks, exploiting the skills learned during the previous phases. The experimental evaluation shows the feasibility and advantage of the proposed method in terms of modularity, low number of demonstrations, and reliability of the trained system.