In this thesis I report the development of FARSA (Framework for Autonomous Robotics Simulation and Analysis), a simulation tool for the study of the interaction between language and action in cognitive robots and more in general for experiments in embodied cognitive science. Before presenting the tools, I will describe a series of experiments that involve simulated humanoid robots that acquire their behavioural and language skills autonomously through a trial-and-error adaptive process in which random variations of the free parameters of the robots’ controller are retained or discarded on the basis of their effect on the overall behaviour exhibited by the robot in interaction with the environment. More specifically the first series of experiments shows how the availability of linguistic stimuli provided by a caretaker, that indicate the elementary actions that need to be carried out in order to accomplish a certain complex action, facilitates the acquisition of the required behavioural capacity. The second series of experiments shows how a robot trained to comprehend a set of command phrases by executing the corresponding appropriate behaviour can generalize its knowledge by comprehending new, never experienced sentences, and by producing new appropriate actions.
Together with their scientific relevance, these experiments provide a series of requirements that have been taken into account during the development of FARSA. The objective of this project is that to reduce the complexity barrier that currently discourages part of the researchers interested in the study of behaviour and cognition from initiating experimental activity in this area. FARSA is the only available tools that provide an integrated framework for carrying on experiments of this type, i.e. it is the only tool that provides ready to use integrated components that enable to define the characteristics of the robots and of the environment, the characteristics of the robots’ controller, and the characteristics of the adaptive process. Overall this enables users to quickly setup experiments, including complex experiments, and to quickly start collecting results
