Publication

Neural computing increases robot adaptivity

Journal Article (2002)

Journal

Natural Computing

Pages

391-425

Volume

1

Number

4

Doc link

http://dx.doi.org/10.1023/A:1021309224128

File

Download the digital copy of the doc pdf document

Abstract

The limited adaptivity of current robots is preventing their widespreadapplication. Since the biological world offers a full range of adaptive mechanisms working at different scales, researchers have turned to it for inspiration. Among the several disciplines trying to reproduce these mechanisms artificially, this paper concentrates on the field of Neural Networks and its contributions to attain sensorimotor adaptivity in robots. Essentially this type of adaptivity requires tuning nonlinear mappings on the basis of input-output information. After briefly reviewing the fundamentals of neural computing, the paper describes several experimental robotic systems relying on the following adaptive mappings: inverse kinematics, inverse dynamics, visuomotor and force-control mappings. Finally, the main trends in the evolution of neural computing are highlighted, followed by some remarks drawn from the surveyed robotic applications.

Categories

robots.

Author keywords

adaptivity, neural networks, robot control, sensorimotor mappings

Scientific reference

C. Torras. Neural computing increases robot adaptivity. Natural Computing, 1(4): 391-425, 2002.