Grounded Human-Robot Interaction

摘要

This paper presents a system for advanced verbal interactions between humans and artificial agents with the aim to learn a simple language in which words and their meaning are grounded in sensory-motor experiences of the agent, and which allows agents to interact and cooperate with humans in shared environments. The system learns grounded language models from examples with a minimum of user intervention and without feedback, and it has been used to understand and subsequently to generate appropriate natural language descriptions of real objects and to engage in verbal interactions with a human partner.