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Proceedings Paper

Can robots learn like people do?
Author(s): Stephen H. Lane; David A. Handelman; Jack J. Gelfand
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Paper Abstract

This paper describes an approach to robotic control patterned after models of human skill acquisition and the organization of the human motor control system. The intent of the approach is to develop autonomous robots capable of learning complex tasks in unstructured environments through rule-based inference and self-induced practice. Features of the human motor control system emulated include a hierarchical and modular organization antagonistic actuation and multi-joint motor synergies. Human skill acquisition is emulated using declarative and reflexive representations of knowledge feedback and feedforward implementations of control and attentional mechanisms. Rule-based systems acquire rough-cut task execution and supervise the training of neural networks during the learning process. After the neural networks become capable of controlling system operation reinforcement learning is used to further refine the system performance. The research described is interdisciplinary and addresses fundamental issues in learning and adaptive control dexterous manipulation redundancy management knowledge-based system and neural network applications to control and the computational modelling of cognitive and motor skill acquisition. 296 / SPIE Vol. 1294 Applications of Artificial Neural Networks (1990)

Paper Details

Date Published: 1 August 1990
PDF: 14 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21181
Show Author Affiliations
Stephen H. Lane, Princeton Univ. (United States)
David A. Handelman, Princeton Univ. (United States)
Jack J. Gelfand, Princeton Univ. (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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