Skill Learning by Autonomous Robotic Playing using Active Learning and Creativity
This addresses the challenge of limited skill adaptability in robotics, offering an incremental improvement for autonomous manipulation tasks.
The paper tackles the problem of autonomous acquisition of manipulation skills by enabling robots to extend their solvable situations through autonomous play, using active learning and creative behavior generation. It shows that this approach allows robots to solve previously-unsolvable tasks like tower disassembly and improves learning speed by around 9% in a book grasping scenario.
We treat the problem of autonomous acquisition of manipulation skills where problem-solving strategies are initially available only for a narrow range of situations. We propose to extend the range of solvable situations by autonomous playing with the object. By applying previously-trained skills and behaviours, the robot learns how to prepare situations for which a successful strategy is already known. The information gathered during autonomous play is additionally used to learn an environment model. This model is exploited for active learning and the creative generation of novel preparatory behaviours. We apply our approach on a wide range of different manipulation tasks, e.g. book grasping, grasping of objects of different sizes by selecting different grasping strategies, placement on shelves, and tower disassembly. We show that the creative behaviour generation mechanism enables the robot to solve previously-unsolvable tasks, e.g. tower disassembly. We use success statistics gained during real-world experiments to simulate the convergence behaviour of our system. Experiments show that active improves the learning speed by around 9 percent in the book grasping scenario.