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Proceedings Paper

Building adaptive connectionist-based controllers: review of experiments in human-robot interaction, collective robotics, and computational neuroscience
Author(s): Aude Billard
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Paper Abstract

This paper summarizes a number of experiments in biologically inspired robotics. The common feature to all experiments is the use of artificial neural networks as the building blocks for the controllers. The experiments speak in favor of using a connectionist approach for designing adaptive and flexible robot controllers, and for modeling neurological processes. I present 1) DRAMA, a novel connectionist architecture, which has general property for learning time series and extracting spatio-temporal regularities in multi-modal and highly noisy data; 2) Robota, a doll-shaped robot, which imitates and learns a proto-language; 3) an experiment in collective robotics, where a group of 4 to 15 Khepera robots learn dynamically the topography of an environment whose features change frequently; 4) an abstract, computational model of primate ability to learn by imitation; 5) a model for the control of locomotor gaits in a quadruped legged robot.

Paper Details

Date Published: 16 October 2000
PDF: 10 pages
Proc. SPIE 4196, Sensor Fusion and Decentralized Control in Robotic Systems III, (16 October 2000); doi: 10.1117/12.403750
Show Author Affiliations
Aude Billard, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 4196:
Sensor Fusion and Decentralized Control in Robotic Systems III
Gerard T. McKee; Paul S. Schenker, Editor(s)

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