A reactive navigation method based on an incremental learning of tasks sequences

Abstract

Within the contest of learning sequences of basic tasks to build a complex behavior, a method is proposed to coordinate a hierarchical set of tasks. Each one possesses a set of sub-tasks lower in the hierarchy, which must be coordinated to respect a binary perceptive constraint. For each task, the coordination is achieved by a reinforcement learning inspired algorithm based on the heuristic which does not need internal parameters. A validation of the method is given, using a simulated Khepera robot. A goal-seeking behavior is divided into three tasks: go to the goal, follow a wall on the left and on the right. The last two tasks utilize basic behaviors and two other sub-tasks: avoid obstacles on the left and on the right. All the tasks may use a set of 5 basic behaviors. The global goal-seeking behavior and the wall-following and the obstacle avoidance tasks are learned during a step by step learning process.