Learning of action patterns and reactive behavior plans via a novel two-layered ethology-based action selection mechanism

The two most important abilities for a robot to survive in a given environment are selecting and learning the most appropriate actions in a given situation. Historically, they have also been the biggest problems in robotics. To help solve this problem, we propose a two-layered action selection mechanism (ASM) which designates an action pattern layer and a reactive behavior plan layer. In the reactive behavior plan layer, a task is selected by comparing behavior motivation values that, in an animal, correspond to external stimuli as well as internal states due to hormones. After a task is selected, its corresponding reactive behavior plan is executed as a set of sequential dynamic behavior motivations (DBMs), each of which is associated with an action pattern. In the action pattern layer, each action pattern can be functionally decomposed into primitive motor actions. Shortest path-based Q-learning (SPQL) is incorporated into both the reactive behavior plan and action pattern layers. In the reactive behavior plan layer, relationships between perceptions and action patterns are learned to satisfy a given motivation, as well as the relative priorities of these relationships. In the action pattern layer, the relations between sensory states and primitive motor actions can be learned. To establish the validity of our proposed ASM, experiments with our real designed robot was illustrated together with simulations.