Hierarchical Interest-Driven Goal Babbling for Efficient Bootstrapping of Sensorimotor skills

摘要

We propose a novel hierarchical online learning scheme for fast and efficient bootstrapping of sensorimotor skills. Our scheme permits rapid data-driven robot model learning in a "learning while behaving" fashion. It is updated continuously to adapt to time-dependent changes and driven by an intrinsic motivation signal. It utilizes an online associative radial basis function network, which is the first associative dynamic network to be constructed from scratch with high stability. Moreover, we propose a parameter-sharing technique to increase efficiency, stabilize the online scheme, avoid exhaustive parameter tuning, and speed up the learning process. We apply our proposed algorithms on a 7-DoF physical robot manipulator and demonstrate their performance and efficiency.