Particle Filter on Episode
This addresses the problem of teach-and-replay tasks for robots, offering a method for behavior replay and recovery, but it appears incremental as it adapts existing particle filter techniques to a new context.
The paper tackles the problem of enabling robots to replay taught behaviors by proposing a particle filter algorithm that operates on recorded experience sequences, called episodes, allowing the robot to find similar situations and replay actions, with the robot demonstrating recovery from skids and interruptions.
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.