A Robotic Scenario for Programmable Fixed-Weight Neural Networks Exhibiting Multiple Behaviors

Artificial neural network architectures are systems which usually exhibit a unique/special behavior on the basis of a fixed structure expressed in terms of parameters computed by a training phase. In contrast with this approach, we present a robotic scenario in which an artificial neural network architecture, the Multiple Behavior Network (MBN), is proposed as a robotic controller in a simulated environment. MBN is composed of two Continuous-Time Recurrent Neural Networks (CTRNNs), and is organized in a hierarchial way: Interpreter Module ( IM ) and Program Module ( PM ). IM is a fixed-weight CTRNN designed in such a way to behave as an interpreter of the signals coming from PM , thus being able to switch among different behaviors in response to the PM output programs . We suggest how such an MBN architecture can be incrementally trained in order to show and even acquire new behaviors by letting PM learn new programs, and without modifying IM structure.