INFANT neural controller for adaptive sensory-motor coordination
References (30)
Prerequisite for development of visually guided reaching in the kitten
Brain Research
(1974)- et al.
Feedback-error-learning neural network for trajectory control of a robotic manipulator
Neural Networks
(1988) - et al.
Corrective movements following refixation saccades, types, and control system analysis
Vision Research
(1972) A New approach to manipulator control: The cerebellar model articulation controller (CMAC)
Journal of Dynamic Systems, Measurement and Control
(1975)- et al.
Encoding of spatial location by posterior parietal neurons
Science
(1985) The parietal lobes
(1982)- et al.
Neural dynamics of adaptive sensory-motor control
(1986/1989) - et al.
Saccadic eye movements to flashed targets
Vision Research
(1976) Recovering spatial motor coordination after visual cortex lesion
Acquiring components of visually guided behavior
A neural model for labile sensorimotor coordinations
Movement-produced stimulation in the development of visually guided behavior
Journal of Comparative Physiological Psychology
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
Journal of Physiology
Generic constraints on underspecitied target trajectories
Cited by (61)
Inverse Kinematics of Dextrous Manipulators
2012, Neural Systems for RoboticsA developmental algorithm for ocular-motor coordination
2010, Robotics and Autonomous SystemsCitation Excerpt :Even when saccades have been learned they have often not been very closely aligned with existing psychological data and knowledge. For example, [39] addressed the problem of driving moveable visual sensors to locate static objects. The emphasis was on topographic mappings and artificial neural networks, but the neural controller needed 100,000 trials during training. [40]
A modular neural network architecture for step-wise learning of grasping tasks
2007, Neural NetworksCitation Excerpt :Recently, another kind of approach has emerged. This new approach is based on the utilization of neural networks to define grasping configurations or to learn the mapping from an object shape to a hand configuration or a grasp choice (Guigon, Grandguillaume, Otto, Boutkhil, & Burnod, 1994; Kuperstein, 1991; Taha, Brown, & Wright, 1997; Uno, Fukumura, Suzuki, & Kawato, 1999). The previous studies emphasize the correspondence between an object and a hand shape.
Categorizing arbitrarily shaped objects based on grasping configurations
2006, Robotics and Autonomous SystemsA neural network model for coordination of hand gesture during reach to grasp
2006, Neural NetworksFrom behaviour-based robots to motivation-based robots
2005, Robotics and Autonomous Systems