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Evolution of prehension ability in an anthropomorphic neurorobotic arm.

Massera G, Cangelosi A, Nolfi S - Front Neurorobot (2007)

Bottom Line: In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment.The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot's body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Science and Technologies, National Research Council (CNR) Italy.

ABSTRACT
In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot's body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules.

No MeSH data available.


Performance of the best evolved robots of the three best replications of the experiment.
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Figure 7: Performance of the best evolved robots of the three best replications of the experiment.

Mentions: Figure 7 shows the average performance of the best evolved robots of three of the best replications of the experiment observed by systematically varying the position of the objects on the table. As can be seen, although different individuals vary with respect to their generalization capabilities, they all display rather good performance on the central diagonal area which corresponds to the preferential trajectory followed by the arm in normal conditions (i.e. when the objects are placed on the central position of the table). The decrease in performance on the top-right and bottom-left part of the table can be explained by considering that grasping objects located in these positions require postures which differ significantly from those assumed by the robots to grasp objects in the central area of the table.


Evolution of prehension ability in an anthropomorphic neurorobotic arm.

Massera G, Cangelosi A, Nolfi S - Front Neurorobot (2007)

Performance of the best evolved robots of the three best replications of the experiment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2533584&req=5

Figure 7: Performance of the best evolved robots of the three best replications of the experiment.
Mentions: Figure 7 shows the average performance of the best evolved robots of three of the best replications of the experiment observed by systematically varying the position of the objects on the table. As can be seen, although different individuals vary with respect to their generalization capabilities, they all display rather good performance on the central diagonal area which corresponds to the preferential trajectory followed by the arm in normal conditions (i.e. when the objects are placed on the central position of the table). The decrease in performance on the top-right and bottom-left part of the table can be explained by considering that grasping objects located in these positions require postures which differ significantly from those assumed by the robots to grasp objects in the central area of the table.

Bottom Line: In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment.The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot's body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Science and Technologies, National Research Council (CNR) Italy.

ABSTRACT
In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot's body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules.

No MeSH data available.