Grounding Perception: A Developmental Approach to Sensorimotor Contingencies
Machine Learning
2018-10-05 v1 Artificial Intelligence
Robotics
Machine Learning
Abstract
Sensorimotor contingency theory offers a promising account of the nature of perception, a topic rarely addressed in the robotics community. We propose a developmental framework to address the problem of the autonomous acquisition of sensorimotor contingencies by a naive robot. While exploring the world, the robot internally encodes contingencies as predictive models that capture the structure they imply in its sensorimotor experience. Three preliminary applications are presented to illustrate our approach to the acquisition of perceptive abilities: discovering the environment, discovering objects, and discovering a visual field.
Keywords
Cite
@article{arxiv.1810.01870,
title = {Grounding Perception: A Developmental Approach to Sensorimotor Contingencies},
author = {Alban Laflaquière and Nikolas Hemion and Michaël Garcia Ortiz and Jean-Christophe Baillie},
journal= {arXiv preprint arXiv:1810.01870},
year = {2018}
}
Comments
8 pages, 4 figures, workshop at IROS 2015 conference