Efficient Online Interest-Driven Exploration for Developmental Robots

A major challenge for online and data-driven model learning in robotics is the high sample-complexity. This hinders its efficiency and practical feasibility for lifelong learning, in particular for developmental robots that autonomously bootstrap their sensorimotor skills in an open-ended environment. In this work, we propose new methods to mediate this problem in order to permit the learning of robot models online, from scratch, and in a learning while behaving fashion. Exploration is utilized and autonomously driven by a novel intrinsic motivation signal which combines knowledge-based and competence-based elements and surpasses other state-of-art methods. In addition, we propose an Episodic Online Mental Replay to accelerate the online learning, to ensure the sample-efficiency, and to update the model online rapidly. The efficiency as well as the applicability of our methods are demonstrated with a physical 7-DoF Baxter manipulator. We show that our learning schemes are able to drastically reduce the sample complexity and learn the data-driven model online, even within a limited time frame.