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Register a new userProbing the in-mouth texture perception with a biomimetic tongue
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An experimental biomimetic tongue-palate system has been developed to probe human in-mouth texture perception. Model tongues are made from soft elastomers patterned with fibrillar structures analogue to human filiform papillae. The palate is represented by a rigid flat plate parallel to the plane of the tongue. To probe the behavior under physiological flow conditions, deflections of model papillae are measured using a novel fluorescent imaging technique enabling sub-micrometer resolution of the displacements. Using optically transparent newtonian liquids under steady shear flow, we show that deformations of the papillae allow determining their viscosity from 1 Pa.s down to the viscosity of water of 1 mPa.s, in full quantitative agreement with a recently proposed model [Lauga et al., Frontiers in Physics, 2016, 4, 35]. The technique is further validated for a shear-thinning and optically opaque dairy system.
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We investigate the transduction of tactile information during active exploration of finely textured surfaces using a novel tactile sensor mimicking the human fingertip. The sensor has been designed by integrating a linear array of 10 micro-force sensors in an elastomer layer. We measure the sensors response to the passage of elementary topographical features in the form of a small hole on a flat substrate. The response is found to strongly depend on the relative location of the sensor with respect to the substrate/skin contact zone. This result can be quantitatively interpreted within the scope of a linear model of mechanical transduction, taking into account both the intrinsic response of individual sensors and the context-dependent interfacial stress field within the contact zone. Consequences on robotics of touch are briefly discussed.
This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.
Water is a ubiquitous liquid with unique physico-chemical properties, whose nature has shaped our planet and life as we know it. Water in restricted geometries has different properties than in bulk. Confinement can prevent low-temperature crystallization into a hexagonal structure, thus creating a state of amorphous water. In this work we introduce a family of synthetic lipids with designed cyclopropyl modification in the hydrophobic chains that exhibit unique liquid-crystalline behaviour at low temperature, enabling maintenance of amorphous water down to 10 K due to nanoconfinement in a bio-mimetic milieu. Small and Wide Angle X-ray Scattering, Elastic and Inelastic Neutron Scattering, Nuclear Magnetic Resonance Spectroscopy and Differential Scanning Calorimetry, complemented by Molecular Dynamics Simulations, unveil a complex lipid/water phase diagram, in which bicontinuous cubic and lamellar liquid crystalline phases containing sub-zero liquid, glassy, or ice water emerge as a competition between the two components, each pushing towards its thermodynamically favoured state.
The modification of the wetting properties of marble surfaces by a biomimetic laser processing approach has been investigated. Marble surfaces have been irradiated with ultrashort laser pulses in different conditions to analyze the effect of laser treatment on their wetting properties with the aim of evaluating its potential for surface protection. The contact angle of a water drop placed on the surface was used to assess the wettability of the processed areas versus the pristine surface. Although the surfaces are initially hydrophilic upon laser treatment, after a few days they develop a strong hydrophobic behavior. The time evolution of the contact angle has been monitored up to 11 months after treatment. A short and a long-term evolution, associated to the combined effect of multi-scale roughness (nano- and micro-roughness) and the attachment of chemical species at the surface over the time, has been observed. Through an analysis of the temporal evolution of surfaces processed with different laser scan line separations, the relative roles of multi-scale roughness and chemical changes has been elucidated and correlated to the induced morphologies. Micro-roughness manifests as a noticeable micro-scale topography modulation, leading to a hydrophobic behavior consistent with the Cassie-Baxter model. In turn, the superimposed nano-roughness leads to an increase of the effective surface area, enhancing the attachment of chemical functional groups over time that progressively modify the surface energy. The durability of the improved surface properties (hydrophobicity) has been tested by measurements of the contact angle of the stabilized (aged) surfaces after cleaning in different conditions with very positive results.
Reproducing the capabilities of the human sense of touch in machines is an important step in enabling robot manipulation to have the ease of human dexterity. A combination of robotic technologies will be needed, including soft robotics, biomimetics and the high-resolution sensing offered by optical tactile sensors. This combination is considered here as a SoftBOT (Soft Biomimetic Optical Tactile) sensor. This article reviews the BRL TacTip as a prototypical example of such a sensor. Topics include the relation between artificial skin morphology and the transduction principles of human touch, the nature and benefits of tactile shear sensing, 3D printing for fabrication and integration into robot hands, the application of AI to tactile perception and control, and the recent step-change in capabilities due to deep learning. This review consolidates those advances from the past decade to indicate a path for robots to reach human-like dexterity.
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