71,214 research outputs found
Symbol Emergence in Robotics: A Survey
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 self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art 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.Comment: submitted to Advanced Robotic
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network
To ensure that a robot is able to accomplish an extensive range of tasks, it
is necessary to achieve a flexible combination of multiple behaviors. This is
because the design of task motions suited to each situation would become
increasingly difficult as the number of situations and the types of tasks
performed by them increase. To handle the switching and combination of multiple
behaviors, we propose a method to design dynamical systems based on point
attractors that accept (i) "instruction signals" for instruction-driven
switching. We incorporate the (ii) "instruction phase" to form a point
attractor and divide the target task into multiple subtasks. By forming an
instruction phase that consists of point attractors, the model embeds a subtask
in the form of trajectory dynamics that can be manipulated using sensory and
instruction signals. Our model comprises two deep neural networks: a
convolutional autoencoder and a multiple time-scale recurrent neural network.
In this study, we apply the proposed method to manipulate soft materials. To
evaluate our model, we design a cloth-folding task that consists of four
subtasks and three patterns of instruction signals, which indicate the
direction of motion. The results depict that the robot can perform the required
task by combining subtasks based on sensory and instruction signals. And, our
model determined the relations among these signals using its internal dynamics.Comment: 8 pages, 6 figures, accepted for publication in RA-L. An accompanied
video is available at this https://youtu.be/a73KFtOOB5
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