@inproceedings{Lee:2011:10.1109/DEVLRN.2011.6037332,
author = {Lee, K and Demiris, Y},
doi = {10.1109/DEVLRN.2011.6037332},
pages = {1--6},
publisher = {IEEE},
title = {Towards incremental learning of task-dependent action sequences using probabilistic parsing},
url = {http://dx.doi.org/10.1109/DEVLRN.2011.6037332},
year = {2011}
}

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TY  - CPAPER
AB  - We study an incremental process of learning where a set of generic basic actions are used to learn higher-level task-dependent 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.
AU  - Lee,K
AU  - Demiris,Y
DO  - 10.1109/DEVLRN.2011.6037332
EP  - 6
PB  - IEEE
PY  - 2011///
SP  - 1
TI  - Towards incremental learning of task-dependent action sequences using probabilistic parsing
UR  - http://dx.doi.org/10.1109/DEVLRN.2011.6037332
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000297472300020&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/19999
ER  - 

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