Abstract
This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.
Highlights
The robotics research covers a wide range of application scenarios, from industrial or service robots up to robotic assistance for disabled or elderly people
We propose Behavior Chain Model (BCM) in order to generalize the form to cope with a variety of similar tasks in similar environment
We proposed a methodology which tries to emulate the human action of vision in a general-conceptual way that includes: primary recognition of object in environment, visual-based mobile robot behavior learning, and prediction of new situations
Summary
The robotics research covers a wide range of application scenarios, from industrial or service robots up to robotic assistance for disabled or elderly people. The predictive model consists of a set of rules (classifiers) which are endowed with an “effect” part, to predict the situation the agent will encounter if the action specified by the rules is executed These systems are able for generalization over the sensory input. In [10], results are presented from experiments with a visually guided four wheeled mobile robot carrying out perceptual judgment based on visual-motor anticipation to exhibit the ability to understand a spatial arrangement of obstacles in its behavioral meaning. The robot learns a forward model by moving randomly within arrangements of obstacles and observing the changing visual input. The most important factor of robot assistance by the behavior sequence learning is the design of interface between neural network and sensors/actuators.
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