  Differently from animals, robots can record its experience correctly for long
time. We propose a novel algorithm that runs a particle filter on the time
sequence of the experience. It can be applied to some teach-and-replay tasks.
In a task, the trainer controls a robot, and the robot records its sensor
readings and its actions. We name the sequence of the record an episode, which
is derived from the episodic memory of animals. After that, the robot executes
the particle filter so as to find a similar situation with the current one from
the episode. If the robot chooses the action taken in the similar situation, it
can replay the taught behavior. We name this algorithm the particle filter on
episode (PFoE). The robot with PFoE shows not only a simple replay of a
behavior but also recovery motion from skids and interruption. In this paper,
we evaluate the properties of PFoE with a small mobile robot.
