Developmental learning for autonomous robots
Robotics and Autonomous Systems
Staged Competence Learning in Developmental Robotics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Towards Learning Robotic Reaching and Pointing: An Uncalibrated Visual Servoing Approach
CRV '09 Proceedings of the 2009 Canadian Conference on Computer and Robot Vision
A developmental algorithm for ocular-motor coordination
Robotics and Autonomous Systems
Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map
Robotics and Autonomous Systems
Novelty and habituation: the driving forces in early stage learning for developmental robotics
Biomimetic Neural Learning for Intelligent Robots
Design of robotic visual servo control based on neural network and genetic algorithm
International Journal of Automation and Computing
Some Basic Principles of Developmental Robotics
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development
Implicit Sensorimotor Mapping of the Peripersonal Space by Gazing and Reaching
IEEE Transactions on Autonomous Mental Development
Cognitive Developmental Robotics: A Survey
IEEE Transactions on Autonomous Mental Development
Integration of Active Vision and Reaching From a Developmental Robotics Perspective
IEEE Transactions on Autonomous Mental Development
A new calibration method for an inertial and visual sensing system
International Journal of Automation and Computing
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The skill of robotic hand-eye coordination not only helps robots to deal with real time environment, but also affects the fundamental framework of robotic cognition. A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination. However, several important features within infant developmental procedure have not been introduced into such approaches. This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants. The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination, and adopts a developmental mechanism from psychology to drive the robot. The entire learning procedure is driven by developmental constraint: The robot starts to act under fully constrained conditions, when the robot learning system becomes stable, a new constraint is assigned to the robot. After that, the robot needs to act with this new condition again. When all the contained conditions have been overcome, the robot is able to obtain hand-eye coordination ability. The work is supported by experimental evaluation, which shows that the new approach is able to drive the robot to learn autonomously, and make the robot also exhibit developmental progress similar to human infants.