Acquiring task models for imitation learning through games with a purpose

Abstract

Teaching robots everyday tasks like making pancakes by instructions requires interfaces that can be intuitively operated by non-experts. By performing novel manipulation tasks in a virtual environment using a data glove task-related information of the demonstrated actions can directly be accessed and extracted from the simulator. We translate low-level data structures of these simulations into meaningful first-order representations whereby we are able to select data segments and analyze them at an abstract level. Hence, the proposed system is a powerful tool for acquiring examples of manipulation actions and for analyzing them whereby robots can be informed how to perform a task.