1,721,089 research outputs found
System Approach: A paradigm for Robotic Tactile Sensing
No full textIn the pursuit of developing touch sensors or tactile
sensing arrays, the emphasis has only been on the sensors. This
led to a large number of ‘bench top’ sensors, very few of which
have actually been used in robotic systems. And those that have
seen the actual use have almost invariably been used in static
contact point imaging rather than the active manipulation or
exploration. Perhaps the lack of the system approach rendered
many of them unusable. In this work, we present the design of a
tactile sensing system taking into account not only the parameters
to be sensed but also the physical and operational constraints of
robotic system
Blink-Sync: Mediating Human-Robot Social Dynamics with Naturalistic Blinking Behavior
No full textThis short paper discusses the utilization of naturalistic eye blinking behavior in social robotics and gives an overview of the application possibilities. It proposes an integrative blinking model and gives an outlook on its implementation
Understanding mirror neurons: a bio-robotic approach
No full textThis paper reports about our investigation on action understanding in the
brain. We review recent results of the neurophysiology of the mirror system
in the monkey. Based on these observations we propose a model of this
brain system which is responsible for action recognition. The link between
object affordances and action understanding is considered. To support our
hypothesis we describe two experiments where some aspects of the model
have been implemented. In the first experiment an action recognition system
is trained by using data recorded from human movements. In the second
experiment, the model is partially implemented on a humanoid robot which
learns to mimic simple actions performed by a human subject on different
objects. These experiments show that motor information can have a significant
role in action interpretation and that a mirror-like representation can be
developed autonomously as a result of the interaction between an individual
and the environment
Incremental semiparametric inverse dynamics learning
No full textThis paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot
Relevance-weighted-reconstruction of articulatory features in deep-neural-network-based acoustic-to-articulatory mapping
No full textWe present a strategy for learning Deep-Neural-Network (DNN)-based Acoustic-to-Articulatory Mapping (AAM) functions where the contribution of an articulatory feature (AF) to the global reconstruction error is weighted by its relevance. We first empirically show that when an articulator is more crucial for the production of a given phone it is less variable, confirming previous findings. We then compute the relevance of an articulatory feature as a function of its frame-wise variance dependent on the acoustic evidence which is estimated through a Mixture Density Network (MDN). Finally we combine acoustic and recovered articulatory features in a hybrid DNN-HMM phone recognizer. Tested on the MOCHA-TIMIT corpus, articulatory features reconstructed by a standardly trained DNN lead to a 8.4% relative phone error reduction (w.r.t. a recognizer that only uses MFCCs), whereas when the articulatory features are reconstructed taking into account their relevance the relative phone error reduction increased to 10.9
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