An Extended Mobile Manipulation Robot Learning Novel Objects

Nagaoka, Tomoaki; Attamimi, Muhammad; Sugiura, Komei; Nagai, Takayuki; Iwahashi, Naoto; Toda, Tomoki; Okada, Hiroyuki; Omori, Takashi

doi:10.7210/jrsj.30.213

Cited by 2 publications

(1 citation statement)

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“…In this context, some competitions including real-world human-robot interaction must be effective for facilitating researchers to study embodied cognitive systems and emergent symbol systems, and for evaluate appropriateness of the developed systems. RoboCup@Home is an obvious candidate [162][163][164][165].…”

Section: Discussionmentioning

confidence: 99%

Symbol Emergence in Robotics: A Survey

Taniguchi

Nagai

Nagaoka

et al. 2015

Preprint

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Humans can learn the use of 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 a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of 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, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially selforganized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-ofart research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain 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 of research in SER.

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Section: Discussionmentioning

confidence: 99%