224 research outputs found
Imitation and Mirror Systems in Robots through Deep Modality Blending Networks
Learning to interact with the environment not only empowers the agent with
manipulation capability but also generates information to facilitate building
of action understanding and imitation capabilities. This seems to be a strategy
adopted by biological systems, in particular primates, as evidenced by the
existence of mirror neurons that seem to be involved in multi-modal action
understanding. How to benefit from the interaction experience of the robots to
enable understanding actions and goals of other agents is still a challenging
question. In this study, we propose a novel method, deep modality blending
networks (DMBN), that creates a common latent space from multi-modal experience
of a robot by blending multi-modal signals with a stochastic weighting
mechanism. We show for the first time that deep learning, when combined with a
novel modality blending scheme, can facilitate action recognition and produce
structures to sustain anatomical and effect-based imitation capabilities. Our
proposed system, can be conditioned on any desired sensory/motor value at any
time-step, and can generate a complete multi-modal trajectory consistent with
the desired conditioning in parallel avoiding accumulation of prediction
errors. We further showed that given desired images from different
perspectives, i.e. images generated by the observation of other robots placed
on different sides of the table, our system could generate image and joint
angle sequences that correspond to either anatomical or effect based imitation
behavior. Overall, the proposed DMBN architecture not only serves as a
computational model for sustaining mirror neuron-like capabilities, but also
stands as a powerful machine learning architecture for high-dimensional
multi-modal temporal data with robust retrieval capabilities operating with
partial information in one or multiple modalities
Embodied Gesture Processing: Motor-Based Integration of Perception and Action in Social Artificial Agents
A close coupling of perception and action processes is assumed to play an important role in basic capabilities of social interaction, such as guiding attention and observation of others’ behavior, coordinating the form and functions of behavior, or grounding the understanding of others’ behavior in one’s own experiences. In the attempt to endow artificial embodied agents with similar abilities, we present a probabilistic model for the integration of perception and generation of hand-arm gestures via a hierarchy of shared motor representations, allowing for combined bottom-up and top-down processing. Results from human-agent interactions are reported demonstrating the model’s performance in learning, observation, imitation, and generation of gestures
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed
The motor theory of speech perception holds that we perceive the speech of
another in terms of a motor representation of that speech. However, when we
have learned to recognize a foreign accent, it seems plausible that recognition
of a word rarely involves reconstruction of the speech gestures of the speaker
rather than the listener. To better assess the motor theory and this
observation, we proceed in three stages. Part 1 places the motor theory of
speech perception in a larger framework based on our earlier models of the
adaptive formation of mirror neurons for grasping, and for viewing extensions
of that mirror system as part of a larger system for neuro-linguistic
processing, augmented by the present consideration of recognizing speech in a
novel accent. Part 2 then offers a novel computational model of how a listener
comes to understand the speech of someone speaking the listener's native
language with a foreign accent. The core tenet of the model is that the
listener uses hypotheses about the word the speaker is currently uttering to
update probabilities linking the sound produced by the speaker to phonemes in
the native language repertoire of the listener. This, on average, improves the
recognition of later words. This model is neutral regarding the nature of the
representations it uses (motor vs. auditory). It serve as a reference point for
the discussion in Part 3, which proposes a dual-stream neuro-linguistic
architecture to revisits claims for and against the motor theory of speech
perception and the relevance of mirror neurons, and extracts some implications
for the reframing of the motor theory
Symbol Emergence in Cognitive Developmental Systems: a Survey
OAPA Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems
Modeling the Development of Goal-Specificity in Mirror Neurons
Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives
Effect of Microwave Frying on Acrylamide Generation, Mass Transfer, Color, and Texture in French Fries
[EN] The objective of this work was to evaluate the effect of microwave power on acrylamide generation, as well as moisture and oil fluxes and quality attributes of microwave-fried potatoes. Concretely, 25 g of potato strips, in 250 mL of fresh oil (at room temperature), were subjected to three different microwave powers (315, 430, and 600 W) in a conventional microwave oven. Microwave frying resulted in an acrylamide reduction ranged from 37 to 83% compared to deep-oil frying. Microwave-fried French fries presented lower moisture and higher fat content than deep-oil fried potatoes. Concretely, microwave-fried potatoes presented values of moisture and texture more similar to potato chips than French fries, nonetheless with lower fat levels (less than 20 g/100 g wb) and acrylamide content (lower than 100 ¿g/kg wb) at the reference time. 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