Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Constructive Incremental Learning from Only Local Information
Neural Computation
Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning
IEEE Transactions on Visualization and Computer Graphics
Dexterous hand-arm coordinated manipulation using active body-environment contact
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A new strategy combining empirical and analytical approaches for grasping unknown 3D objects
Robotics and Autonomous Systems
Planning 3D regrasp operations with a polyarticulated mechanical hand
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
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Reach and grasp are the two key functions of human prehension. The Central Nervous System controls these two functions in a separate but interdependent way. The choice between different solutions to reach and grasp an object-provided by multiple and redundant degrees of freedom (dof)-depends both on the properties and on the use (affordance) of the object to be manipulated. This same control paradigm, i.e. subdivision of prehension into reach and grasp as well as the corresponding multimodal (sensory/motor) information fusion schemes, can also be applied to a mechanical hand carried by a robotic arm. The robotic arm will then be responsible for positioning the hand with respect to the object, and the hand will then grasp and manipulate the object. In this article, we present a biomimetic sensory-motor control scheme in the aim of providing an object-dependent and intelligent reach and grasp ability to such systems. The proposed model is based on a multi-network architecture which incorporates multiple Matching Units trained by a statistical learning algorithm (LWPR). Matching Units perform a multimodal signal integration by correlating sensory and motor information analogous to that observed in cerebral neuronal networks. The simulated network of multiple Matching Units provided estimations of object-dependent 5-finger grasp configurations with endpoint positional errors in the order of a few millimeters. For validation, these estimations were then applied to the control of movement kinematics on an experimental robot composed of a 6 dof robot arm carrying a 16 dof mechanical 4-finger hand. Precision of the kinematics control was such that successful reach, grasp and lift was obtained in all the tests.