WayEx: Waypoint Exploration Using a Single Demonstration

We propose WayEx, a new method for learning complex goal-conditioned robotics tasks from a single demon- stration. Our approach distinguishes itself from existing imita- tion 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 expan- sion 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. Appendix is available at: https://waypoint-ex.github.io.