Paper Info
Title
Vision-sensorimotor abstraction and imagination towards exploring robot's inner world
Abstract
Based on indications from the neuroscience and psychology, both perception and action can be internally simulated by activating sensor and motor areas in the brain without external sensory input or without any resulting overt behavior. This hypothesis, however, can be highly useful in the real robot applications. The robot, for instance, can cover some of the corrupted sensory inputs by replacing them with its internal simulation. The accuracy of this hypothesis is strongly based on the agent's experiences. As much as the agent knows about the environment, as much as it can build a strong internal representation about it. Although many works have been presented regarding to this hypothesis with various levels of success. At the sensorimotor abstraction level, where extracting data from the environment occur, however, none of them have so far used the robot's vision as a sensory input. In this study, vision-sensorimotor abstraction is presented through memory-based learning in a real mobile robot "Hemisson" to investigate the possibilities of explaining its inner world based on internal simulation of perception and action at the abstract level. The analysis of the experiments illustrate that our robot with vision sensory input has developed some kind of simple associations or anticipation mechanism through interacting with the environment, which enables, based on its history and the present situation, to guide its behavior in the absence of any external interaction.
Year
DOI
Venue
2008
10.1109/IJCNN.2008.4634134
IJCNN
Keywords
Field
DocType
cognition,control engineering computing,learning (artificial intelligence),mobile robots,robot vision,visual perception,Hemisson robot,memory-based learning,mobile robot,perception simulation,robot inner world,robot vision,vision-sensorimotor abstraction
Computer science,Human–computer interaction,Artificial intelligence,Abstraction layer,Robot,Artificial neural network,Sensory system,Cognition,Perception,Mobile robot,Visual perception,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-4393
0
0.34
References 
Authors
6
4
Name
Order
Citations
PageRank
Fady Alnajjar16612.23
Abdul Rahman Hafiz2224.08
Indra Bin Mohd Zin3192.33
Kazuyuki Murase4103875.66