WayEx: Waypoint Exploration using a Single Demonstration
This addresses the problem of data efficiency and action information requirements in robotics for researchers and practitioners, though it appears incremental.
The paper tackles learning complex goal-conditioned robotics tasks from a single demonstration, achieving a 50% reduction in training time compared to traditional reinforcement learning and higher rewards than existing imitation learning methods.
We propose WayEx, a new method for learning complex goal-conditioned robotics tasks from a single demonstration. Our approach distinguishes itself from existing imitation learning methods by demanding fewer expert examples and eliminating the need for information about the actions taken during the demonstration. This is accomplished by introducing a new reward function and employing a knowledge expansion technique. We demonstrate the effectiveness of WayEx, our waypoint exploration strategy, across six diverse tasks, showcasing its applicability in various environments. Notably, our method significantly reduces training time by 50% as compared to traditional reinforcement learning methods. WayEx obtains a higher reward than existing imitation learning methods given only a single demonstration. Furthermore, we demonstrate its success in tackling complex environments where standard approaches fall short. More information is available at: https://waypoint-ex.github.io.