Symbol emergence in robotics: a survey
Symbol emergence in robotics: a survey
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DOI：
10.1080/01691864.2016.1164622
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发表时间：
2016-01-01
影响因子：
2
通讯作者：
Asoh, Hideki
中科院分区：
文献类型：
--
作者：
Taniguchi, Tadahiro;Nagai, Takayuki;Asoh, Hideki
人类可以通过与环境的身体互动以及与他人的符号沟通来学习语言。对人类如何形成符号系统并通过自主心理发展获得符号系统的计算理解非常重要。最近，已经进行了许多有关建造机器人系统和机器学习方法的研究，这些方法可以通过与环境和其他系统的多模式相互作用来学习语言。了解人类？ - 社交互动和开发可以长期与人类用户进行平稳通信的机器人需要了解符号系统的动态。参与者的具体认知和社会互动逐渐以建设性的方式改变符号系统。在本文中，我们介绍了一个研究领域，称为机器人技术（SER）中的符号出现。 SER代表了符号出现系统的建设性方法。符号出现系统是通过符号通信和与自主认知发展剂（即人类和发展机器人）的身体互动来自我组织的。在本文中，具体来说，我们描述了有关SER的一些最新研究主题，例如多模式分类，单词发现和双重发音分析。它们使机器人能够以完全不受监督的方式从原始的感觉运动信息中发现单词及其体现的含义，包括视觉信息，触觉信息，听觉信息和声音信号。最后，我们建议未来的SER研究方向。
Humans can learn a language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form symbol systems and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted regarding the construction of robotic systems and machine learning methods that can learn a language through embodied multimodal interaction with their environment and other systems. Understanding human?-social interactions and developing a robot that can smoothly communicate with human users in the long term require an understanding of the dynamics of symbol systems. The embodied cognition and social interaction of participants gradually alter a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER represents a constructive approach towards a symbol emergence system. The symbol emergence system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e. humans and developmental robots. In this paper, specifically, we describe some state-of-art research topics concerning SER, such as multimodal categorization, word discovery, and double articulation analysis. They enable robots to discover words and their embodied meanings from raw sensory-motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions for research in SER.