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ITALK 项目:一种发展机器人学方法,用于研究个体、社会和语言学习。

The ITALK project: a developmental robotics approach to the study of individual, social, and linguistic learning.

机构信息

ITALK Project.

出版信息

Top Cogn Sci. 2014 Jul;6(3):534-44. doi: 10.1111/tops.12099. Epub 2014 Jun 17.

Abstract

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.

摘要

本文介绍了一个多学科研究项目的成果,该项目将语言知识整合并转移到机器人中,作为研究人类和人形机器人语言发展的实证范例。在人类语言和认知发展的框架内,我们关注三种核心学习类型如何相互作用和共同发展:个体对自身身体和环境的学习、社会学习(向他人学习)和语言能力的学习。我们主要关注的是,这些能力如何在不断的反馈循环中相互支撑彼此的发展,因为它们的相互作用使代理人在与他人互动和操作其世界的能力方面产生越来越复杂的能力。实验结果与人类语言和认知发展的里程碑进行了总结,并表明社会学习、个体学习和语言能力的相互支撑为每个领域的学习创造了背景、条件和要求。本文还讨论了该研究项目中确定的挑战和见解,以及它们对认知科学和语言发生的可能和实际贡献。最后,提出了未来工作的方向,以继续发展这种方法,为理解这些相互支撑的过程提供一个综合框架,作为人类和机器人语言发展的基础。

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