707 research outputs found
Construction of a memory model using neural networks and its application to a humanoid robot
制度:新 ; 文部省報告番号:甲2424号 ; 学位の種類:博士(工学) ; 授与年月日:2007/3/1 ; 早大学位記番号:新451
The development of numerical cognition in children and artificial systems: a review of the current knowledge and proposals for multi-disciplinary research
Numerical cognition is a distinctive component of human intelligence such that the observation of
its practice provides a window into high-level brain function. The modelling of numerical abilities in artificial
cognitive systems can help to confirm existing child development hypotheses and define new ones by
means of computational simulations. Meanwhile, new research will help to discover innovative principles
for the design of artificial agents with advanced reasoning capabilities and clarify the underlying algorithms
(e.g. deep learning) that can be highly effective but difficult to understand for humans.
This article promotes new investigation by providing a common resource for researchers with different
backgrounds, including computer science, robotics, neuroscience, psychology, and education, who are
interested in pursuing scientific collaboration on mutually stimulating research on this topic. The article
emphasises the fundamental role of embodiment in the initial development of numerical cognition in
children. This strong relationship with the body motivates the Cognitive Developmental Robotics (CDR)
approach for new research that can (among others) help to standardise data collection and provide open
databases for benchmarking computational models. Furthermore, we discuss the potential application of
robots in classrooms and argue that the CDR approach can be extended to assist educators and favour
mathematical education
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
Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning
There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.The work was funded by FCT - Fundacao para a Ciencia e Tecnologia, through the PhD Grants SFRH/BD/48529/2008 and SFRH/BD/41179/2007 and Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN (nr. 289146) and the FCT-Research Center CMAT (PEst-OE/MAT/UI0013/2014)
Introduction: The Third International Conference on Epigenetic Robotics
This paper summarizes the paper and poster contributions
to the Third International Workshop on
Epigenetic Robotics. The focus of this workshop is
on the cross-disciplinary interaction of developmental
psychology and robotics. Namely, the general
goal in this area is to create robotic models of the
psychological development of various behaviors. The
term "epigenetic" is used in much the same sense as
the term "developmental" and while we could call
our topic "developmental robotics", developmental
robotics can be seen as having a broader interdisciplinary
emphasis. Our focus in this workshop is
on the interaction of developmental psychology and
robotics and we use the phrase "epigenetic robotics"
to capture this focus
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