Autonomous reconstruction of state space for learning of robot behavior

Abstract

When an autonomous robot is to learn its behavior, whether an appropriate state space is available or not is a critical issue for the flexibility and efficiency of the learning process. What is problematic is that it is usually very difficult to prepare such an ideal state space manually beforehand. We propose a new state space "reconstruction" method. With this, behavior-based robots can autonomously "rebuild" their state spaces after they accumulate behavior experience using initial state spaces. This reconstruction approach is more advantageous than the conventional state space construction methods or incremental state partitioning methods in that it achieves both the efficiency in the learning process and the optimality of the resultant behavior performance.