Our paper "Interest-Driven Exploration with Observational Learning for Developmental Robots" by Rania Rayyes, Heiko Donat, Jochen Steil and Michael Spranger, has been accepted at IEEE Transactions on Cognitive and Developmental Systems. The paper establishes new learning scheme integrating intrinsic motivation with learning from observation to accelerate the autonomous learning of developmental robots in real-world applications. There is hardly any research in the literature to integrate intrinsic motivation with learning from an interacting teacher for developmental robots. In this work, the robot is able to establish its motor coordination driven by its curiosity and additionally benefit from observing human demonstrations' outcomes. This work has been developed during R.Rayyes’ research internship at Sony Computer Science Laboratories in Tokyo and the experiment with a physical 7-DoF Baxter robot has been conducted at IRP Lab. This work is within very few work in this field which is demonstrated on a real robot due to the remarkable achieved sample-efficiency.