Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A resource-allocating network for function interpolation
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Neural Computation
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
Neurocomputing
The cog project: building a humanoid robot
Computation for metaphors, analogy, and agents
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
Developmental learning for autonomous robots
Robotics and Autonomous Systems
Staged Competence Learning in Developmental Robotics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning robotic hand-eye coordination through a developmental constraint driven approach
International Journal of Automation and Computing
Hi-index | 0.00 |
Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.