Learning Forward Models for Robots

Dearden, Anthony M.; Demiris, Yiannis

Learning Forward Models for Robots

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Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1440-1444.

Abstract

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.

Anthony M. Dearden, and Yiannis Demiris. 2005. Learning Forward Models for Robots. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 1440–1444. https://ijcai.org/proceedings/2005/163

@inproceedings{ijcai2005p163,
  title     = {Learning Forward Models for Robots},
  author    = {Dearden, Anthony M. and Demiris, Yiannis},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-05}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {1440--1444},
  year      = {2005},
  url       = {https://ijcai.org/proceedings/2005/163},
}

TY  - CPAPER
TI  - Learning Forward Models for Robots
AU  - Dearden, Anthony M.
AU  - Demiris, Yiannis
T2  - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2005
SP  - 1440
EP  - 1444
UR  - https://ijcai.org/proceedings/2005/163
ER  -