Learning real manipulation tasks from virtual demonstrations using LSTM. (arXiv:1603.03833v2 [cs.RO] CROSS LISTED)

Robots assisting disabled or elderly people in activities of daily living must perform complex manipulation tasks. These tasks are dependent on the user's environment and preferences. Thus, learning from demonstration (LfD) is a promising choice that would allow the non-expert user to teach the robot different tasks. Unfortunately, learning general solutions from raw demonstrations requires a significant amount of data. Performing this number of physical demonstrations is unfeasible for a disabled user. In this paper we propose an approach where the user demonstrates the manipulation task in a virtual environment. The collected demonstrations are used to train an LSTM recurrent neural network that can act as the controller for the robot. We show that the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot.

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