A Formalism for Learning from Demonstration*-Reference-Cited by-同舟云学术

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A Formalism for Learning from Demonstration*

Published:2010-01-01 Issue:1 Volume:1 Page:1-13
ISSN:2081-4836
Container-title:Paladyn, Journal of Behavioral Robotics
language:
Short-container-title:

Author:

Billing Erik A.,Hellström Thomas

Abstract

AbstractThe paper describes and formalizes the concepts and assumptions involved in

Publisher

Walter de Gruyter GmbH

Subject

Behavioral Neuroscience,Artificial Intelligence,Cognitive Neuroscience,Developmental Neuroscience,Human-Computer Interaction

Link

http://link.springer.com/content/pdf/10.2478/s13230-010-0001-5.pdf

Reference87 articles.

1. A. Alissandrakis, C. L. Nehaniv, and K. Dautenhahn. Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 32:482–496, 2002.

2. A. Alissandrakis, C. L. Nehaniv, and K. Dautenhahn. Action, state and effect metrics for robot imitation. In 15th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN 2006), pages 232–237, Hatfield, September 2006.

3. R. Amit and M. Mataric. Parametric primitives for motor representation and control. In Int. Conf. on Robotics and Automation (ICRA), Washington DC, May 2002.

4. B. D. Argall, S. Chernova, M. Veloso, and B. Browning. A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5):469–483, May 2009.

5. R. C. Arkin. Behaviour-Based Robotics. MIT Press, 1998.

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