Gianluca Massera
2011
Autonomous Mental Development, IEEE Transactions on 3(2):176--189, 2011
Abstract Populations of simulated agents controlled by dynamical neural networks are trained by artificial evolution to access linguistic instructions and to execute them by indicating, touching, or moving specific target objects. During training the agent ...
2006
Developing a reaching behaviour in an simulated anthropomorphic robotic arm through an evolutionary techniquePDF
Artificial Life X, pages 234-240, 2006
In this article we present an evolutionary technique for developing a neural network based controller for an an- thropomorphic robotic arm with 4 DOF able to exhibit a reaching behaviour. Evolved neural controllers display an ability to reach targets accurately and generalize ...MORE ⇓
In this article we present an evolutionary technique for developing a neural network based controller for an an- thropomorphic robotic arm with 4 DOF able to exhibit a reaching behaviour. Evolved neural controllers display an ability to reach targets accurately and generalize their ability to moving targets. This study demonstrates that it is possible to obtain solutions that are extremely parsimonious from the point of view of the control system. Evolutionary training techniques allow us to evolve parameters of the control system on the basis of the global effects that they produce on the dynamics arising from the interaction between the control system, the robot's body and the environment.