ON OBSERVATIONAL LEARNING OF HIERARCHIES IN SEQUENTIAL TASKS: A DYNAMIC NEURAL FIELD MODEL

Abstract

Many of the tasks we perform during our everyday lives are achieved through sequential execution of a set of goal-directed actions. Quite often these actions are organized hierarchically, corresponding to a nested set of goals and subgoals. Several computational models address the hierarchical execution of goal directed actions by humans. However, the neural learning mechanisms supporting the temporal clustering of goal-directed actions in a hierarchical structure remain to a large extent unexplained. In this paper we investigate in simulations, of a dynamic neural field (DNF) model, biologically-based learning and adaptation mechanisms that can provide insight into the development of hierarchically organized internal representations of naturalistic tasks. In line with recent experimental evidence from observational learning studies, the DNF model implements the idea that prediction errors play a crucial role for grouping fine-grained events into larger units. Our ultimate goal is to use the model to endow the humanoid robot ARoS with the capability to learn hierarchies in sequential tasks, and to use that knowledge to enable efficient collaborative joint tasks with human partners. For testing the ability of the system to deal with the real-time constraints of a learning-by-demonstration paradigm we use the same assembly task from our previous work on human-robot collaboration. The model provides some insights on how hierarchically structured task representations can be learned and on how prediction errors made by the robot and signaled by the demonstrator can be used to control such process.