Staged Competence Learning in Developmental Robotics

Authors:
Mark H. Lee;Qinggang Meng;Fei Chao
Affiliations:
Department of Computer Science, University of Wales, Aberystwyth, UK;Department of Computer Science, Loughborough University, Leicestershire, UK;Department of Computer Science, University of Wales, Aberystwyth, UK
Venue:
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Year:
2007

Citing 6
Cited 5

Made-up minds: a constructivist approach to artificial intelligence

Made-up minds: a constructivist approach to artificial intelligence
Scaling up sensorimotor systems: constraints from human infancy

Adaptive Behavior
Reaching movements: implications of connectionist models

The handbook of brain theory and neural networks
Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought

Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought
Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Connection Science
Novelty and habituation: the driving forces in early stage learning for developmental robotics

Biomimetic Neural Learning for Intelligent Robots

A developmental algorithm for ocular-motor coordination

Robotics and Autonomous Systems
A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures

Neurocomputing
Review: learning like a baby: A survey of artificial intelligence approaches

The Knowledge Engineering Review
A biologically constrained architecture for developmental learning of eye---head gaze control on a humanoid robot

Autonomous Robots
Learning robotic hand-eye coordination through a developmental constraint driven approach

International Journal of Automation and Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Developmental psychology has long recognized the presence of stages in human cognitive development, although the underlying causes and processes are still an open question and subject to much debate. This article draws inspiration from psychology and describes an approach towards developmental growth for robotics that utilizes natural constraints in a general learning mechanism. The method, summarized as Lift-Constraint, Act, Saturate (LCAS), is applicable to all levels of control and behavior, and can be implemented in any robotic configuration. An implementation based on sensory-motor learning in early infancy is described and the results from experiments are presented and discussed.