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1.	Laschi, Cecilia, et al. (författare) A Bio-inspired Neural Sensory-Motor Coordination Scheme for Robot Reaching and Preshaping 2006 Ingår i: Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics : BioRob 2006. - Piscataway, NJ : IEEE. - 1424400406 ; , s. 531-536 Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract We present a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach for and to grasp an object, while pre-shaping the fingers to the required grasp configuration. A model for sensory-motor coordination derived from studies in humans inspired the development of the scheme. A special feature of this model is the prediction of the tactile image perceived after grasping. The proposed scheme is based on a neuro-fuzzy modnle that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for grasping, selects the best-suited hand configuration, and predicts the tactile feedback after grasping. The implementation of the scheme on a humanoid robot ailowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.

Laschi, Cecilia, et al. (författare)
A Bio-inspired Neural Sensory-Motor Coordination Scheme for Robot Reaching and Preshaping
2006
Ingår i: Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics : BioRob 2006. - Piscataway, NJ : IEEE. - 1424400406 ; , s. 531-536
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach for and to grasp an object, while pre-shaping the fingers to the required grasp configuration. A model for sensory-motor coordination derived from studies in humans inspired the development of the scheme. A special feature of this model is the prediction of the tactile image perceived after grasping. The proposed scheme is based on a neuro-fuzzy modnle that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for grasping, selects the best-suited hand configuration, and predicts the tactile feedback after grasping. The implementation of the scheme on a humanoid robot ailowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.

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