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Acquiring task models for imitation learning through games with a purpose

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3 Author(s)
Lars Kunze ; Intell. Robot. Lab., Univ. of Birmingham, Birmingham, UK ; Andrei Haidu ; Michael Beetz

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.

Published in:

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Date of Conference:

3-7 Nov. 2013