Linking perception and action in a control architecture for human-robot domains

摘要

Human-robot interaction is a growing research domain; there are many approaches to robot design, depending on the particular aspects of interaction being focused on. In this paper we present an action-based framework that provides a natural means for robots to interact with humans and to learn from them. Perception and action are the essential means for a robot's interaction with the environment; for successful robot performance it is thus important to exploit this relation between a robot and its environment. Our approach links perception and actions in a unique architecture for representing a robot's skills (behaviors). We use this architecture to endow the robots with the ability to convey their intentions by acting upon their environment and also to learn to perform complex tasks from observing and experiencing a demonstration by a human teacher. We demonstrate these concepts with a Pioneer 2DX mobile robot, learning various tasks from a human and, when needed, interacting with a human to get help by conveying its intentions through actions.