Towards incremental learning of task-dependent action sequences using probabilistic parsing

TitleTowards incremental learning of task-dependent action sequences using probabilistic parsing
Publication TypeConference Proceedings
Year of Conference2011
AuthorsLee, K, Demiris Y
Conference NameIEEE First Joint International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB 2011),
Volume2
Pagination1-6
Date Published08/2011
Conference LocationFrankfurt am Main, Germany
Abstract

We study an incremental process of learning where a set of generic basic actions are used to learn higher-level taskdependent action sequences. A task-dependent action sequence is learned by associating the goal given by a human demonstrator with the task-independent, general-purpose actions in the action repertoire. This process of contextualization is done using probabilistic parsing. We propose stochastic context-free grammars as the representational framework due to its robustness to noise, structural flexibility, and easiness on defining task-independent actions. We demonstrate our implementation on a real-world scenario using a humanoid robot and report implementation issues we had.

DOIDOI: http://dx.doi.org/10.1109/DEVLRN.2011.6037332

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