@inproceedings{Dearden:2005,
author = {Dearden, A and Demiris, YK},
pages = {1440--1445},
publisher = {International Joint Conferences on Artificial Intelligence},
title = {Learning forward models for robots},
url = {http://ijcai.org/Past%20Proceedings/IJCAI-05/IJCAI-05%20CONTENT.htm},
year = {2005}
}

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TY  - CPAPER
AB  - Forward models enable a robot to predict the effects of its actions on its own motor system and its environment. This is a vital aspect of intelligent behaviour, as the robot can use predictions to decide the best set of actions to achieve a goal. The ability to learn forward models enables robots to be more adaptable and autonomous; this paper describes a system whereby they can be learnt and represented as a Bayesian network. The robot’s motor system is controlled and explored using 'motor babbling'. Feedback about its motor system comes from computer vision techniques requiring no prior information to perform tracking. The learnt forward model can be used by the robot to imitate human movement.
AU  - Dearden,A
AU  - Demiris,YK
EP  - 1445
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2005///
SP  - 1440
TI  - Learning forward models for robots
UR  - http://ijcai.org/Past%20Proceedings/IJCAI-05/IJCAI-05%20CONTENT.htm
UR  - http://hdl.handle.net/10044/1/12700
ER  - 

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