Discrete Task-Space Automatic Curriculum Learning for Robotic Grasping

Deep reinforcement learning algorithms struggle in the domain of robotics where data collection is time consuming and in some cases safety-constrained. For sample-efficiency, curriculum learning has shown good results in deep learning-based methods. However, the issue lies on the generation of the curriculum itself, which the field of automatic curriculum learning is trying to solve. We present an automatic curriculum learning algorithm for discrete task-space scenarios. Our curriculum generation is based on difficulty measure between tasks and learning progress metric within a task. We apply our algorithm to a grasp learning problem involving 49 diverse objects. Our results show that a policy trained based on a curriculum is both sample efficient compared to learning from scratch and able to learn tasks that the latter could not learn within a reasonable amount of time.