Evolution of prehension ability in an anthropomorphic neurorobotic arm
- 1
Institute of Cognitive Science and Technologies, National Research Council (CNR), Italy
- 2
School of Computing, Communications and Electronics, University of Plymouth, UK
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.
Keywords:
robotic arm, reaching and grasping, adaptation, evolutionary robotics
Citation:
Gianluca Massera, Angelo Cangelosi and Stefano Nolfi (2007). Evolution of prehension ability in an anthropomorphic neurorobotic arm. Front. Neurorobot. 1:4. doi: 10.3389/neuro.12/004.2007
Received: 6 September 2007;
Paper pending published: 8 October 2007;
Accepted: 12 October 2007;
Published online: 2 November 2007.
Edited by:
Frederic Kaplan, Ecole Polytechnique Federale De Lausanne, Switzerland
Reviewed by:
Jun Tani, RIKEN Brain Science Institute, Saitama, Japan
Simon Bovet, University of Zurich, Switzerland
Copyright:
© 2007 Massera, Cangelosi, Nolfi. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
*Correspondence:
Angelo Cangelosi, School of Computing, Communications and Electronics, University of Plymouth, Drake Circus, Plymouth, UK. e-mail: acangelosi@plymouth.ac.uk