# Particle Filter on Episode

## TL;DR

This paper introduces PFoE, a novel algorithm that applies particle filtering to sequences of recorded robot experiences, enabling effective teach-and-replay tasks with behavior recovery capabilities.

## Contribution

The paper presents PFoE, a new particle filter algorithm that operates on experience episodes for robot behavior reproduction and recovery.

## Key findings

- PFoE effectively finds similar situations from experience episodes.
- PFoE enables robots to recover from skids and interruptions.
- PFoE improves behavior replay accuracy in experiments.

## Abstract

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.