Between Exploration and Exploitation in Motor Babbling

Motor babbling allows an agent sampling trajectory data without a priori knowledge about self-body and environment dynamics. We discuss about the efficiency of motor babbling through the example of drawing task. In authors’ insight, natural motor babbling may be featured by exploration and exploitation processes. From this idea, we propose exploitation babbling and ε -greedy babbling. In order to implement the proposed babblings, we developed dynamics learning tree (DLT). DLT is an online incremental learning algorithm that has constant calculation order O(1). The proposed exploitation babbling and ε -greedy babbling improved the rate of effective data at 8 and 7 % from previous babbling respectively. ε -greedy babbling converged its prediction error fastest among the three babblings. Using ε -greedy babbling, a humanoid robot with wired flexible fingers successfully drew a figure without a priori knowledge about the dynamics among self-body, pen, and pen tablet.