88,692 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
Multimodal Hierarchical Dirichlet Process-based Active Perception
In this paper, we propose an active perception method for recognizing object
categories based on the multimodal hierarchical Dirichlet process (MHDP). The
MHDP enables a robot to form object categories using multimodal information,
e.g., visual, auditory, and haptic information, which can be observed by
performing actions on an object. However, performing many actions on a target
object requires a long time. In a real-time scenario, i.e., when the time is
limited, the robot has to determine the set of actions that is most effective
for recognizing a target object. We propose an MHDP-based active perception
method that uses the information gain (IG) maximization criterion and lazy
greedy algorithm. We show that the IG maximization criterion is optimal in the
sense that the criterion is equivalent to a minimization of the expected
Kullback--Leibler divergence between a final recognition state and the
recognition state after the next set of actions. However, a straightforward
calculation of IG is practically impossible. Therefore, we derive an efficient
Monte Carlo approximation method for IG by making use of a property of the
MHDP. We also show that the IG has submodular and non-decreasing properties as
a set function because of the structure of the graphical model of the MHDP.
Therefore, the IG maximization problem is reduced to a submodular maximization
problem. This means that greedy and lazy greedy algorithms are effective and
have a theoretical justification for their performance. We conducted an
experiment using an upper-torso humanoid robot and a second one using synthetic
data. The experimental results show that the method enables the robot to select
a set of actions that allow it to recognize target objects quickly and
accurately. The results support our theoretical outcomes.Comment: submitte
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