  Machine learning, artificial intelligence and especially deep learning based
approaches are often used to simplify or eliminate the burden of programming
industrial robots. Using these approaches robots inherently learn a skill
instead of being programmed using strict and tedious programming instructions.
While deep learning is effective in making robots learn skills, it does not
offer a practical route for teaching a complete task, such as assembly or
machine tending, where a complex logic must be understood and related sub-tasks
need to be performed. We present a model similar to an episodic memory that
allows robots to comprehend sequences of actions using single demonstration and
perform them properly and accurately. The algorithm identifies and recognizes
the changes in the states of the system and memorizes how to execute the
necessary tasks in order to make those changes. This allows the robot to
decompose the tasks into smaller sub-tasks, retain the essential steps, and
remember how they have been performed.
