An episodic long-term memory for robots: The bender case

Abstract

The main goal of this paper is to propose a framework for providing an episodic long-term memory for a robot, which includes methods for acquiring, storing, updating, managing and using episodic information. This will give a robot the ability to incorporate past experiences when interacting with humans, so that the data that the robot learns transcends each session, and thus gives continuity to its activities and behaviors. As a proof of concept, the implementation of an episodic long-term memory for the Bender robot is described. This includes the implementation and evaluation of a behavior called Conversation, which allows Bender to interact with people using the information stored in the episodic memory.