In this section
@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}
}
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 -
