  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.
